dataesr/scientific-paragraphs-categorization huggingface dataset:
Model comparison We tried to ensure a fair comparison between the two approaches. There are potentially many ways to make such a comparison, which could focus on explanatory power, predictive power, ease of use, and/or computational requirements. Going into the study, however, we already knew that the GLM-GSSL hybrid was oriented more toward prediction than explanation. As a result, our main point of comparison quantitatively and analytically was predictive power. Following Klassen et al. 11  , we used mean absolute deviation (MAD) to compare the predictive performance of the two approaches. The MAD statistic was calculated during leave-one-out cross-validation analyses for each approach. Leave-one-out cross validation (LooCV) is a common method for estimating predictive power. It is performed by dropping an observation (a temple, in this case) from a training dataset, using the remaining data to estimate model parameters, and then making a prediction for the dropped observation [Ref.
43 , 241-245]. After a prediction is made, the difference (deviation) between the prediction and the true observed value for a given dropped data point is calculated. Then the dropped observation is returned to the training dataset, and the process is repeated for each observation in turn. This results in a list of differences, one for each observation in the training dataset. The mean of these differences is calculated, which produces an estimate for average outof-sample predictive error of the relevant model. The mean of the deviations (the MAD) and the distribution of deviations for the two approaches were compared to see which, if either, approach produced better average out-of-sample predictions. For the Bayesian model, we used the mean of the posterior age estimates from the relevant MCMC sample chain for the given dropped temple to make the comparisons.
We used RJ-MCMC to estimate the parameters of the Bayesian model for a training dataset that included only temples with no missing data. We then extracted the posterior inclusion probabilities for each predictor and examined the values. At the same time, we were able to directly examine the impact of temple morphology on the age estimates. As explained, the morphotypes entered into the Bayesian model as an index variable, which ultimately acted as the intercept in a set of parallel regressions. As a result, the posterior distribution for each morphotype parameter indicated the marginal mean effect of that morphotype on temple age. We plotted the posterior distributions associated with each morphotype in order to examine their individual contributions to temple age for each type-effectively, the average baseline foundation date associated with each morphotype.
Acknowledgements WCC, PR, and HG received financial support from the  Max Planck Society. SK received support from the  Marie Sklodowska-Curie Postdoctoral Fellowship  ( 896092 ) and a  Canadian Social Sciences and Humanities Research Council Insight Development  Grant. DE received support from  European Research Council  ( 639828 ,  866454 ). PR received support from the  European Research Council  ( 850709 ).
Purpose Multiple different materials are used for filling bone defects following bone-patellar tendon-bone (BPTB) graft ACL reconstruction surgery. The theoretical objective being to minimize kneeling pain, improve clinical outcomes and reduce anterior knee pain following surgery. The impact of these materials is assessed in this study. Methods A prospective monocentric cohort study was conducted from January 2018 to March 2020. There were 128 skeletally mature athletic patients who underwent ACL reconstruction using the same arthroscopic-assisted BPTB technique, with a minimum follow-up of two years identified in our database. After obtaining approval from the local ethics committee, 102 patients were included in the study. Patients were divided into three groups based on type of bone substitute.
The Bioactive glass 45S5 ceramic Glassbone™ (GB), collagen and hydroxyapatite bone void filler in sponge form Collapat® II (CP), and treated human bone graft Osteopure®(OP) bone substitutes were used according to availability. Clinical evaluation of patients at follow-up was performed using the WebSurvey software. A questionnaire completed in the 2nd post-operative year included three items: The ability to kneel, the presence of donor site pain, and the palpation of a defect. Another assessment tool included the IKDC subjective score and Lysholm score. These two tools were completed by patients preoperatively, and postoperatively on three occasions (6 months, 1 year, and 2 years). Results A total of 102 patients were included in this study. In terms of Kneeling pain, the percentage of GB and CP patients' who kneel with ease were much higher than that of OP patients (77.
Materials and methods A prospective single-center cohort study of the French prospective ACL Study [FAST] (NCT02511158) was performed, including all patients who performed ACLR using BPTB autograft between 2018 and 2020 by 4 senior surgeons. The study was approved by the local ethics committee (Comité de Protection des Personnes IDF VI), and informed consent was obtained from all patients. A retrospective analysis of the prospectively filled data, with a minimum follow-up of two years was performed. One hundred and two patients undergoing ACL reconstruction using BPTB autograft were assessed. Clinical evaluation of patients at follow-up was performed by the surgeons and data was entered in the WebSurvey software. The inclusion criteria were ACL reconstruction using the BTBP technique, athletes, a minimum of two years of post-operative follow-up and an age over 18 years.
Patient follow-up and data collection Three tools were used for data collection at various time points. First, a questionnaire assessed the international knee documentation committee (IKDC)  [24]  subjective score and Lysholm score  [25]. These two tools were completed by patients at four time points: first pre-operatively, and at 6 months, one year and two years postoperatively. Another standardized questionnaire was sent by email to the participants 4 months postoperatively. This was repeated at the 6 months, one year and two years marks following surgery. This questionnaire was made available online via a link to the WebSurvey software (websurvey. fr). If patients failed to answer, a first reminder was made via email, and a second by telephone call. Finally, a questionnaire was sent at the second post-operative year.
Statistical analysis All statistical analyses were performed using the IBM SPSS statistics software. Categorical variables were summarized as frequencies and percentages. Continuous variables were presented as means, standard deviations and ranges. One-way ANOVA was used to compare the mean IKDC and Lysholm scores, as well as the change in these scores between the three groups. Repeated measures ANOVA was used to compare the IKDC and Lysholm scores at different time points within each group. Pearson's Chi-square test or Fisher exact test were used to assess the association of gender, ability to kneel, and internal pain between the three groups. All tests were two-sided and a p < 0. 05 was considered statistically significant.
Structural modelling Using AlphaFold  19  (https://github. com/deepmind/alphafold), structures were predicted for both wild-type R. leguminosarum RirA and for its N8D variant. However, this otherwise powerful tool does not yet allow modelling of protein-bound metal sites, probably explaining the low reliability score for the predicted protein The same side chain conformation for Asp8 would not be possible in NsrR because of its collision with Val91 (substituted by Ala89 in RirA). Substitution of Asn8 by Asp is clearly feasible, and we note the relatively few residue differences in the immediate cluster environments of NsrR and RirA, consistent with the observed spectroscopic similarities between N8D RirA and wild-type NsrR. In contrast, substitution of RirA Asn8 with Cys would not result in its coordination to Fe1 unless there was a significant protein conformational change.
MS data were acquired over the m/z range of 1500-3500 continuously for 5 min using Bruker oTOF Control soware, with parameters as follows: dry gas ow 4 L min -1 , nebuliser gas pressure 0. 8 bar, dry gas 180 °C, capillary voltage 4000 V, offset 500 V, ion energy 5 eV, collision RF 600 Vpp, collision cell energy 10 eV. Processing and analysis of MS experimental data were carried out using Compass DataAnalysis version 4. 1 (Bruker Daltonik, Bremen, Germany). Neutral mass spectra were generated using the ESI Compass version 1. 3 Maximum Entropy deconvolution algorithm over a mass range of 17 300-18 000 Da for the monomer and 34 850-35 810 Da for the dimer. Exact masses are reported from peak centroids representing the isotope average neutral mass. For apo proteins, these are derived from m/z spectra, for which peaks correspond to [M + nH] n+ /n.
Forms of RirA containing a degraded Fe-S cluster, or lacking a cluster entirely, bind the IRO motif with lower affinity, and may also be subject to degradation, resulting in increased expression. O 2 /oxidative stress destabilises the RirA cluster, leading to increased turnover of [4Fe-4S] RirA even under iron sufficiency. ANR-18-CE11-0010 from the Agence Nationale pour la Recherche. We thank Dr Myles Cheesman (UEA) for access to the CD spectrometer, and UEA for funding the purchase of the Q-TOF MS instrument.
, 2019) , in support of GBD 2017. M 3 Fusion bias corrects and combines multiple chemical transport models by finding an optimal linear combination of models for each world region, using weighting based on performance when compared to available observations. This multimodel composite was then corrected within 2 of a monitoring station using a spatial interpolation of observations, creating fine resolution output. To support GBD 2019, we then improved this method using a novel combination of Bayesian Maximum Entropy (BME) along with M 3 Fusion  (DeLang et al. , 2021). BME is a framework for nonlinear geostatistics that performs the fusion of data from multiple sources  (Serre and Christakos, 1999; Christakos et al. , 2001; Christakos et al. , 2004). BME used observations to correct the M 3 Fusion multimodel composite smoothly in both space and time, so that ozone estimates match the observations at station locations.
The influence each station exerts diminishes over time and space based on a calculated spatiotemporal covariance function. BME had been used before on smaller scales to fuse ozone observations and models  (Christakos et al. , 2004; de Nazelle et al. , 2010; Xu et al. , 2016) , but  DeLang et al. (2021)  were the first to apply it globally. The ability of observations in BME to influence estimates across time was also shown to be useful in informing earlier years before new stations were added  (DeLang et al. , 2021). DeLang et al. (2021)  showed that ozone estimates improved markedly over purely model-or observationbased estimates and produced a global fine resolution (0. 1 ) dataset for each year from 1990 to 2017. However, like  Chang et al. (2019)  and  DeLang et al. (2021)  used the M 3 Fusion method, which uses linear model bias corrections that are homogeneous across continents.
, 2017) , which applies CAMP regionally at individual points based on the nearest observations. CAMP and RAMP corrections are applied to the M 3 Fusion multimodel composite prior to BME data fusion. While BME is not restricted spatially, ozone's steep covariance curve means that observations have little influence beyond about 0. 5 from a monitoring station  (DeLang et al. , 2021). In contrast, CAMP and RAMP corrections can influence estimates over a larger spatial range. Specifically, we aim to use regional differences in model underestimation/overestimation to correct the M 3 Fusion results regionally and increase the fidelity of our estimation in areas with sparse or no ozone observation stations, and we apply CAMP and RAMP here to improve estimates in particular beyond about 0. 5 from a monitor.
, 2017)  is applied globally for the first time, with each model grid cell correction based on a unique area that includes the nearest points in space/time. These areas are much smaller than the continental regions used in M 3 Fusion, allowing us to better correct biases in the M 3 Fusion multimodel composite at points far from observations, while BME then applies corrections near observations. In applying RAMP at a global scale, we also make a novel modification of the RAMP method because station observations are sparse in some regions and clustered in others. This modification prevents sharp spatial changes in corrections when transitioning between 2 different regions with dense observation networks. The CAMP-and RAMP-corrected estimates are then each used as global background ozone levels (the global offset) for BME data fusion with observations, as was done by  DeLang et al.
(2021)  with the uncorrected M 3 Fusion multimodel composite. We aim for the improved global ozone database to be useful for researchers in a variety of fields, including atmospheric science, epidemiology and health impact assessment, and ecosystem and crop impacts. As this is the first global application of CAMP and RAMP, we document the methods and results to inform future global data fusion efforts for ozone and other air pollutants. Finally, by further developing geostatistical methods for global ozone estimates in this study, we also aim to provide ozone estimates that can be compared with recent machine learning applications in future studies.
Ozone observations and model estimates We use the ozone season daily maximum 8-h mixing ratio (OSDMA8) as the annual ozone metric, as it is used for calculating health outcomes from ozone pollution by GBD (GBD 2019 Risk Factors Collaborators, 2020) using concentration-mortality relationships from  Turner et al. (2016)  and other studies. OSDMA8 is the maximum 6-month running mean of monthly averages of the daily 8-h maximum mixing ratios. Since ozone is usually highest in the summer, each defined year includes up to March of the following year to capture the Southern Hemisphere summer. All reported ozone values, including observations, modeled values, and estimates, are OSDMA8 values. Ground-level ozone measurements are taken from databases compiled by the Tropospheric Ozone Assessment Report (TOAR) and the Chinese National Environmental Monitoring Center (CNEMC)  (Schultz et al.
, 2017; Lu et al. , 2020). The TOAR database is the largest collection of global hourly surface ozone concentrations and spans 1970-2015. To support this project, some national datasets were extended for  2015 -2017 (DeLang et al. , 2021)). While observations are dense in North America, Europe, Japan, and South Korea, they are sparse to nonexistent elsewhere (Figure  S1 ). CNEMC provides surface ozone observations in China beginning in 2013, and data through 2017 are used in this study  (Lu et al. , 2020). Both datasets were quality controlled with the same algorithm developed for the TOAR database. The number of observation locations in the combined dataset is least in 1990 (with 1,190) while 2015 has the most (4,999). We used surface concentration output from 9 atmospheric chemistry models to create our M 3 Fusion multimodel composite  (DeLang et al.
Data fusion methods M 3 Fusion was used to evaluate model performance and create a bias-corrected multimodel composite for each year 1990-2017  (Chang et al. , 2019). This method finds a linear combination of the 9 models in each year and continental domain that minimizes the mean square error (MSE) compared to interpolated observations, creating a single bias-corrected global composite for each year. This is the same composite used by  DeLang et al. (2021) , and it reflects model expectations about variations in ozone due to emissions, geography, meteorology, and chemistry. However, M 3 Fusion does not capture the nonlinearity of model performance with respect to model value, nor how model performance varies within a continental domain (heterogeneity), both of which we address using RAMP.
Areas that are more than 25 away from these station clusters, like the area at the midpoint between China and Europe, will have a RAMP weight close to 0 and λ Ã 1 ðpÞ % yðpÞ, thereby mitigating any RAMP discontinuity. We call the global output of λ Ã 1 values as weighted RAMP (wRAMP). BME data fusion is then applied after RAMP correction to fuse model prediction and observations, using the approach described by DeLang et al. (2021). Each BME estimate uses a different background assumption for global ozone levels at every grid cell, which we call the global offset, based on either the M 3 Fusion composite, CAMP-corrected M 3 Fusion, or wRAMP-corrected M 3 Fusion. This global offset is corrected using BME so that the final BME estimate matches observed values at each station location. Observations are treated as "hard data," meaning they are perfectly accurate, neglecting measurement uncertainty.
The general knowledge base characterizing X ðpÞ consists of a mean assumed to equal 0 within the estimation neighborhood, and a covariance function obtained from a variogram analysis (see Supplementary information for details on the covariance model and its parameters). The site-specific knowledge consists of the offset-removed observations treated as hard data (data with no assumed uncertainty). We numerically implement BME using the BMElib library written in the MATLAB programming language  (Serre and Christakos, 1999; Christakos et al. , 2001) , and as shown by  DeLang et al. (2021) , in this case, the BME posterior probability density function of X ðp k Þ is Gaussian with a mean xk and associated error variance varðx k Þ equal to that of simple kriging. Finally, the BME estimate ỹk of Y ðp k Þ, representing ozone at the estimation point, and associated variance varðx k Þ are obtained as ỹk ¼ xk þ oðp k Þ; and varðỹ k Þ ¼ varðx k Þ, where oðp k Þ is the (M 3 Fusion, RAMP or wRAMP) offset at the estimation point.
Cross validation Leave-one-out cross-validation (LOOCV) was done by removing each observation one at a time and using various estimation methods to evaluate our ability to predict this observation based on the remaining data. LOOCV was performed by predicting ozone at each 0. 5 grid cell containing an observation point and comparing it with the observations ŷðp i Þ in the grid cell, following the same protocols as  DeLang et al. (2021). This was done for M 3 Fusion, CAMP, and wRAMP both before and after data fusion with BME. For LOOCV of BME, BME was used to estimate each removed point in turn, and the aggregated errors were used to calculate MSE and the Pearson correlation coefficient squared (R 2 ) that is bounded between 0 and 1. For LOOCV on the offsets, the difference between the offset and observation point at each station location was used.
This situation arises because there are no/few local observations for the RAMP correction in this area. To avoid these discontinuities and to avoid RAMP bias corrections based only on measurements far away, we weight RAMP (Figure  2 ) to allow a smooth transition between regions, using weights for RAMP and M 3 Fusion that vary spatially and temporally (Figure  6 ). wRAMP heavily favors RAMP over M 3 Fusion in areas with high density of observations, and RAMP maintains some influence up to 25 from any station. This distance is long enough to give RAMP an influence in areas not reached by the BME correction, but short enough that it creates smooth transitions between regions and lessens the discontinuities seen in pure RAMP. Using wRAMP as our global offset and station observations as hard data for BME, we obtain yearly estimates of global ozone and variance at 0.
BME using M 3 Fusion as Offset: BME data fusion using the M 3 Fusion multimodel composite as the global offset, matching the results of  DeLang et al. (2021). BME using CAMP as Offset: BME data fusion using CAMP as the global offset. BME using wRAMP as Offset: BME data fusion using wRAMP as the global offset. In LOOCV, M 3 Fusion, CAMP, and wRAMP all improve performance over the simple model mean (Table  S2 ), and CAMP and wRAMP provide clear improvements with reduced MSE and improved R 2 over M 3 Fusion. Using BME, LOOCV performance does not differ whether M 3 Fusion, CAMP, and wRAMP are used as the global offset. This result is expected because most observations are clustered, and BME predicts accurately when observations are close together, similar to kriging on the observations. In an LOOCV, removing a single monitor in many cases leaves a sufficient number of nearby stations to accurately predict ozone.
Methods other than LOOCV are therefore important to test the improvement in ozone estimates when wRAMP bias correction is used before BME data fusion, and, for this, we use CBCV. In particular, CBCV more meaningfully tests each method's ability to estimate ozone where there is not a dense network of observations (Figure  8 ). At small box sizes, CBCV approximates LOOCV and all BME methods have similar R 2 (though a smaller R 2 than in LOOCV since CBCV removes observations in all years). As the box size increases, R 2 for wRAMP decreases less than the other cases, maintaining a minimum of 0. 45 at large box sizes. It also does not experience the dramatic performance dropoff that CAMP and M 3 Fusion have at about 4. CAMP also has consistently higher R 2 than M 3 Fusion at box sizes greater than 4.
Conclusions Here, we improve upon the global ozone estimates of  Chang et al. (2019)   We found that performance of M 3 Fusion varies by space/ time location and is often nonlinear, making RAMP the ideal tool to further improve this composite. This method also takes full advantage of TOAR and CNEMC observations, as it allows them to both directly correct estimates locally through time with BME data fusion and inform model corrections at a larger regional scale through M 3 Fusion and RAMP. Our final estimates provide yearly fine resolution global ozone estimates for 1990-2017, involving a data fusion of surface observations from global monitoring stations and 9 chemistry-climate models. We also provide estimates of variance generated by BME, which may be useful for some further applications, but we caution that the variance estimated here does not represent the full uncertainty in ozone concentration.
RAMP could potentially encounter this issue for any dataset where there are 2 or more heavily clustered regions of observations separated by areas with sparse observations. Therefore, we choose to weight RAMP to create smooth transitions between regions, giving a much greater weight to the multimodel composite when corrections are far from observations, while very close to observations corrections are incorporated by BME data fusion. In areas with a more uniform distribution of observations, such as those from previous studies using RAMP  (Xu et al. , 2016; Reyes et al. , 2017) , weighting RAMP would not be necessary. Weighting RAMP by distance from observations avoids such streaks, and limits RAMP's influence in regions very far from monitors. It also allows a smooth transition between the regional bias corrections of RAMP, and M 3 Fusion, which bias corrects within each continent.
Changes in ozone estimates due to including RAMP before BME data fusion are as large as 3 ppb in individual grid cells and absolute values of changes are up to 1 ppb when averaged over regions where ozone monitors are dense. These changes are smaller (<0. 2 ppb in individual grid cells) in regions far from monitors, where the weighting put on RAMP goes to near 0. Because the BME method provides both ozone estimates and the associated variance, we can evaluate the confidence that ozone is above or below selected values. We find that most of the world's population lives in areas very likely above 35 ppb in OSDMA8 and even above 45 ppb. Some regions estimated to have the highest ozone, including much of India, are very likely above 55 ppb. In the case of India, model estimates suggest high ozone that may be above 65 ppb, but the lack of ground observations decreases confidence in this region.