dataesr/scientific-paragraphs-categorization huggingface dataset:
ELT forecast Here we assume a future dataset to be put together by the high-resolution ultra-stable spectrograph currently known as HIRES  (Marconi et al. 2020 ), that will operate the 39. 3 meter Extremely Large Telescope. For simplicity we assume a set of 50 α measurements, uniformly spaced in the redshift range 0. 7 ≤ z ≤ 3. 2, each with an uncertainty of 0. 03 parts per million, which is commensurate with the assumptions in  Leite et al. (2016)  and the Top-Level Requirements for the instrument  (Liske et al. 2014). With these specifications in hand, we generate the fiducial redshift dependence of ∆α/α using Eqs. (  6 ) and  (7)  with two different fiducial cosmologies, dubbed ΛCDM and ζw 0 w a CDM, shown in Table  1. These two fiducial cosmologies correspond to a standard case in which no α variation is present (ΛCDM) and to one where instead we assume the coupling parameter ζ is non vanishing, but still compatible with the laboratory constraints discussed in Sect.
Parameter symbol ΛCDM ζw 0 w a CDM Ω m 0. 32 0. 32 h 0. 67 0. 67 w 0 -1. -0. 94 w a 0 0. 1 ζ 0 -5 × 10 -8 of α varies in redshift and we aim at finding signatures of such variation. Once the fiducial behaviour for ∆α/α is obtained as discussed earlier, we then create the corresponding mock dataset drawing the data points at each redshift from a Gaussian distribution centred at the fiducial model and with σ the expected observational error of HIRES. We assume such errors to be uncorrelated, which observationally is a safe assumption since each measurement comes from a high-resolution (R ∼ 100 000) signal-to-noise limited spectrum of a point source along a different line of sight.
Euclid forecast methodology and Fisher matrices As previously discussed, observations from Large Scale Structures probe are not very sensitive to variations of α and therefore they cannot significantly constrain the coupling ζ. They are however crucial to break the degeneracies between the coupling and the cosmological parameters, and can be therefore combined with the datasets discussed above. In this work we consider specifically Euclid as our probe of LSS. It is worth mentioning that, strictly speaking, LSS probes can provide some relevant constraints on variations of α. In  Albareti et al. (2015) , for example, constraints on ∆α/α were provided using the Oiii doublet from BOSS DR12 quasar spectra. Following similar approaches, we could extract information on the variation of α from the future Euclid data.
We sample the matter density parameter Ω m , the Hubble constant H 0 , the two parameters of the CPL parameterization w 0 and w a , and the coupling parameter ζ that connects the dynamical dark energy scalar field to the electromagnetic sector (see Sect. 2). The posterior distribution P (θ) that we reconstruct with this method at each point θ = (Ω m , H 0 , w 0 , w a , ζ) of the parameter space contains information coming from both α measurements and the Euclid survey, P (θ) ∝ L α (θ)L Euclid (θ) , (14) where L α and L Euclid are the likelihoods of the α and Euclid datasets respectively, and we assumed that the two probes are uncorrelated. The Euclid likelihood is constructed using the Fisher matrix F described in Sect. 3. 3, and it simply exploits the Gaussian assumption done to obtain these: -ln L Euclid ∝ 1 2 (θ -θ fid ) T F(θ -θ fid ), ( 15 ) where F is the Fisher matrix F marginalized over all parameters that are not contained in our sampled parameter space, while θ fid is the fiducial cosmology under examination, which can be one of the two shown in Table  1.
We then use 300 realizations and we calculate both the mean and variance of the integral, with the latter then included, via error propagation, in the error estimate of all reconstructed quantities. For the atomic clocks and the MICROSCOPE bound we use Eq. (  10 ), Eq. (  11 ) and the Euclid ζw 0 w a CDM fiducial in a similar fashion. After this is done, we can compare the predictions of the GA with the data and the fitness of every test function in the population can be calculated via a standard χ 2 statistic. Subsequently, the crossover and mutation operators are applied to a subset, usually the ∼ 30% best-fitting functions in every generation. These are chosen with the tournament selection -see  Bogdanos & Nesseris (2009)  for more details. We repeat this process thousands of times in order to ensure convergence and we also test our fits with several different random seeds, so as not to bias the results.
4 ± 8. 9 ) × 10 -8 when the Euclid optimistic configuration is used. The reason for such loosening of the bound is due to the ability of Euclid to tightly constrain the CPL parameters around the ΛCDM limit (w 0 , w a ) = (-1, 0); due to the degeneracy with ζ -see Eq. (  6 ) and Eq. (  7 ) -this makes the α data less sensitive to the coupling parameter, as now a wider range of values is able to fit the data. Such a result is compatible with what was found in  Calabrese et al. (2014) , where other datasets able to tightly constrain the CPL parameters were considered. In the bottom panel of Fig. 1 , it can be seen however how the weaker constrain on ζ does not lead to a larger spread of the allowed ∆α/α reconstructions, which are instead tightly constrained around the no-variation limit by the inclusion of Euclid information, exactly because of the tight constraints on the CPL parameters.
the degeneracy between these and ζ. Here the loosening of the constraint on the coupling parameter is reduced, with the bound changing from ζ = 0. 1 +3. 5 -3. 9 × 10 -8 to ζ = ( -3. 6 ± 5. 9 ) × 10 -8 (pessimistic) and ζ = ( -3. 8 ± 4. 9 ) × 10 -8 (optimistic), and the constraint on the redshift trend of ∆α/α is tightened around a non-vanishing variation of the fine-structure constant. The different behaviour of the bounds on ζ when Euclid is included in the analysis is due here to the fact that the LSS information constrain the w 0 and w a parameters away from the ΛCDM limit; this implies that the degeneracy shown in the previous case is broken, and the α data do not have a larger range of allowed coupling value. While one can see that the inclusion of Euclid data will help α measurements constrain the variation of this fundamental parameter (since it provides information on the cosmological parameters), the synergy between these two datasets goes both ways: given the assumption done in Sect.
Bayesian evidence The results discussed in this Section make use of Metropolis-Hastings (MH) algorithm to sample the parameter space. This algorithm might fail in reconstructing the posterior shape when this is multimodal. Given the behaviour of some of our posterior distributions we compare the results obtained through MH with those from a nested sampling approach, using the public polychord code  (Handley et al. 2015a,b)  available in Cobaya, finding compatible results. Fig. 3. Same as Fig. 1 , but here the purple contours and lines refer to the forecast ELT data, while the yellow and cyan refer to the combination of this simulated dataset with pessimistic and optimistic Euclid results. The fiducial used here for both ELT and Euclid is the ΛCDM one shown in Table  1. Table  4. Mean values and 68% c.
Synergy between Euclid and ELT Next, we consider the scenario of combining Euclid data with the forecast ELT data, in the case where the fiducial model has a non-zero coupling. In the top panel of Fig. 6  we show the GA reconstruction of the coupling ζ as a function of redshift (red line) using the forecast ELT α measurements, together with the Euclid forecast constraints with a ζw 0 w a CDM fiducial. We use the same atomic clocks constraints and the MICROSCOPE as in the previous subsection. We find that the GA reconstruction is consistent with the fiducial value (shown with the dot-dashed line) within the 1σ bound. In particular, at high redshifts (z > 1) a deviation from zero is detected, in agreement with the fiducial value used in our mocks, highlighting the importance of the extension of the redshift lever arm provided by the ELT measurements of α.
Such a Bayesian ratio can be used for model comparison between the case in which the two datasets are used to fit the same set of cosmological parameter, Z(Webb + recent), and the case in which these might differ for the two datasets, Z(Webb)Z(recent). We obtain these values using polychord to sample our free parameters and, when considering only astrophysical α measurements, we find that ln K ≈ 0. 2, a value that is not able to provide any conclusive evidence for either the concordance or discordance of the data. When instead we include the information brought by local measurements, we obtain ln K ≈ 1. 6, thus an improved significance toward the concordance of the data. Such an increase in the value of ln K comes from the fact that the local measurements dominate the constraints obtained through the parametric approach, thus hiding any possible discordance of the two sets of astrophysical data.
This does not apply in our null test reconstruction of ζ(z); here, the local measurements only contribute to the very low redshift reconstruction, while astrophysical data dominate at higher redshift. Because of this tension between the Webb archival data and the recent dedicated measurement, the reconstruction performed with the GA attempts to be in agreement with both datasets, and this can potentially increase the error on the reconstructed function. One thus expects that removing one of the datasets, the error on the reconstruction will decrease. In particular, in Fig. A. 1 we show the GA reconstruction of the relative variation of the fine structure constant ∆α/α as a function of redshift. The green line corresponds to the dedicated measurements, the orange line to the Webb archival data and the magenta line to the combination of the two, while in all cases the shaded region is the 1σ error.
As expected, the reconstruction of the dedicated measurements has a smaller error (green shaded region), but when we combine them with the Webb archival data then the combined error region not only does not decrease, but instead increases due to the tension, as observed in the magenta shaded region. Equivalently, we see that when we remove the Webb data from the combination of the two, counter-intuitively the error of the reconstruction decreases. However, overall the reconstructions are still compatible with each other and with zero. of the European Commission through grant 776247. SN acknowledges support from the research project PGC2018-094773-B-C32, the Centro de Excelencia Severo Ochoa Program SEV-2016-059 and the Ramón y Cajal program through Grant No. RYC-2014-15843. The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Academy of Finland, the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the Centre National d'Etudes Spatiales, the Deutsches Zentrum für Luft-und Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciência e a Tecnologia, the Ministerio de Economia y Competitividad, the National Aeronautics and Space Administration, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency.
Acknowledgements. MM has received the support of a fellowship from  "la Caixa" Foundation  ( ID 100010434 ), with fellowship code  LCF/BQ/PI19/11690015 , and the support of the  Centro de Excelencia Severo Ochoa Program   SEV-2016-059. The work of CJM was financed by  FEDER -Fundo Europeu de Desenvolvimento Regional  funds through the  COMPETE 2020 -Operational Programme for Competitiveness and Internationalisation (POCI) , and by  Portuguese funds through FCT -Fundação para a Ciência e a Tecnologia  in the framework of the project  POCI-01-0145-FEDER-028987. IT acknowledges support from the  Spanish Ministry of Science, Innovation and Universities  through grant  ESP2017-89838 , and the  H2020  programme.
Time-Resolved SAXS at ID02 Beamline ESRF Prehydrolysed TEOS was prepared by mixing TEOS and ultrapure H 2 O 1:1 and the addition of HCl to catalyse the reaction. This was added to 10 mL of 1 mg/mL TTCF solution, once both phases turned homogenous, at 1:50 ratio at pH 7 in a glass beaker. Using a sterile syringe, 1 mL of sample was taken and injected into a quartz capillary. SAXS measurements were performed on the time-resolved ultra-small-angle scattering beamline ID02 at the European Synchrotron Research Facility (ESRF), Grenoble, France  [23]. The incident X-ray energy was 12. 46 keV and sample-detector distance was employed at 1. 5 m. SAXS data were acquired using the Rayonix MX-170HS detector with exposure times between 0. 01 and 0. 03 s. The measured 2D patterns after normalisation by incident flux, sample transmission, and the solid angle were azimuthally averaged to obtain the 1D static scattering profiles as a function of the magnitude of scattering vector, q.
Where q is given by, q = 4π sin θ λ , with λ the incident X-ray wavelength (=0. 0996 nm) and θ the scattering angle. This gave a q-range of 0. 006 < q < 0. 5 A -1. The scattering background in each case was measured using a Tris buffer, and the normalised background-subtracted data are represented by I(q).
SAXS Data Fitting The previous investigation via SAXS enabled us to categorise the ensilication process into three distinct stages (Figure  1 ). We modelled the system, using the SasView software package  [24] , according to a monodisperse particulate solution initially  [25] , ∆I(q) = N • I 0 • ∆ρ 2 • V 2 particle • P(q) • S(q) to which we add silicic acid in the form of hydrolysed TEOS where I: intensity; N: number of particles; P(q): form factor; S(q): structure factor; ρ: scattering length density; V: particle volume. pliedChem 2021, 1, FOR PEER REVIEW 3.
SAXS Data Fitting The previous investigation via SAXS enabled us to categorise the ensilication process into three distinct stages (Figure  1 ). We modelled the system, using the SasView software package  [24] , according to a monodisperse particulate solution initially  [25] , Δ𝐼(𝑞) = 𝑁 ⋅ 𝐼 ⋅ Δ𝜌 ⋅ 𝑉 ⋅ 𝑃(𝑞) ⋅ 𝑆(𝑞) to which we add silicic acid in the form of hydrolysed TEOS where I: intensity; N: number of particles; P(q): form factor; S(q): structure factor; : scattering length density; V: particle volume. At standard conditions, the ensilication process rapidly transitions between three distinct stages, plotted in I(q) vs. q. TTCF protein (mesh) has several positively charged residues on its surface. Upon addition of hydrolysed TEOS (silicic acid), these polymeric associates are attracted by the positively charged regions (orange line, green circles, stage I).
Ethics and patients This is a prospective single-center parallel randomized study conducted in St Antoine University Hospital (Assistance-Publique Hôpitaux de Paris). All methods were carried out in accordance with relevant guidelines and regulations and with CONSORT recommendations  [14]. Ethical committee approval for this study (Ethical committee n°13,887-P120702) was provided by the Ethical Committee: CPP (Comite de Protection des Personnes) Ile de France V, 184 rue du Fbg St Antoine, Paris, France (Chaiperson Prof JJ Boffa) on 2 April 2013. The study was registered in ClinicalTrials. gov (Clinical Trials identifier: NCT 02036736). Patients less than 75 years old, undergoing hip or knee arthroplasty under general anaesthesia were eligible in the study. Patients with preoperative dementia (defined as a Mini Mental State evaluation (MMS)  [15]  of 24 or less), unable to perform the cognitive tests, or who received preoperative psychotropic agents, as well as obese patients (BMI > 35 kg.
Postoperative analgesia was multimodal. The use of locoregional techniques for post-operative analgesia was encouraged (nerve block, trunk block +/-placement of a perineural catheter +/-wound infiltration). During the stay in PACU, if NRS ≥ 3, morphine was administered by titration (bolus of 1 mg IV repeated every 5 min until NRS at rest < 3). After arrival in the PACU, the Aldrete score was checked every 5 min. Once the score of ≥9 had been attained, the cognitive tests were carried out for a second time. The data from these tests was collected by the same investigator as the day before surgery in the case report form.
Given the pilot data obtained in our department, the difference in combined Z-score was 1. 1. Therefore a total number of 30 patients per group made it possible to highlight a difference of 20% (i. e. 0. 9) in the Z score with an α risk of 5% and a β risk of 10%. Enrolled patients who did not participate further in the study were excluded for final analysis. Results are presented in medians [interquartils] or mean ± SD. The threshold for statistical significance was set at P < 0. 05.
Results A total of 60 patients was enrolled and randomly allocated to Desflurane (n = 30) or Propofol (n = 30) subgroups. Five patients were excluded for final analysis in the Desflurane group (2 for missing data and 3 for protocol violation), and 3 in the Propofol group (1 for missing data and 2 for protocol violation) (Fig. 1 ). Demographic data are presented in Table  1  and did not statistically differ between groups. Intraoperative and postoperative data did not differ between the 2 groups (Table  2 ). Preoperative Mini Mental State (MMS) was 29  [27] [28] [29]  and 29  [28] [29] [30]  respectively in the Desflurane and Propofol groups. Preoperative anxiety, measured using the Amsterdam Preoperative Anxiety and Information Scale (APAIS) was 14  [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]  and 15  [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]  respectively in the Desflurane and Propofol groups (no significant difference).
Cognitive functions Results in medians [interquartils] DSST Digit Symbol Substitution Test; VLT Verbal Learning Test * statistical analysis comparing Desflurane and Propofol subgroups des Personnes Ile de France V, 184 rue du Fbg St Antoine, Paris, France (Chaiperson Prof JJ Boffa) on 2 April 2013. The study was registered in ClinicalTrials. gov (Clinical Trials identifier: NTC 02036736).
Importantly, these differences were consistent with expected differences in relative N and P availability in each study location: volcanic high (elevation)-islands such as Mo'orea should be more N limited  31, 32  , and carbonate low (elevation)-islands, such as those in The Bahamas should be more P limited  [33] [34] [35]  due to the underlying geology  31. To understand why species in these regions exhibit different chemical traits beyond that expected by phylogenetic relatedness, we need to understand what aspects of these species' assemblages may be driving this variation across regions. A unique feature of our dataset is that of the 41 families present, 11 are found in both locations, accounting for 62 and 77% of all individuals in the Polynesian and Caribbean datasets, respectively. Previous research has shown that there is little chemical trait variation within families relative to chemical trait variation among families  19, 22, 23, 37, 38.
3C ). Taken collectively, these results suggest that: (1) using the full dataset, chemical traits between the two regions differed beyond what was expected based only on phylogenetic relatedness, (2) these differences align with differences in nutrient limitation in our study locations, and (3) these differences are not explained by changes in closely related speciesand thus likely did not occur over shorter ecological time periods. These findings provide further support that is consistent across all analyses in our study that chemical trait variation is shaped more by evolutionary history than by ecological factors.
Reduced phylogenetic trait conservatism may be expected when competitive interactions among closely related species are more intense since competition can lead to evolutionary divergence via ecological character displacement  42, 43  (though the generality of this is contentious  44  , particularly with the longer timescales relevant to our study  45  ). Our data may provide indirect evidence of this relationship. Fishes in the Polynesian fish assemblage tend to be slightly closer related than those in the Caribbean (mean pairwise phylogenetic distance was 7% shorter in Polynesia), but are more speciose, and thus Polynesia has more species partitioning the energetic base of coral reef food webs. Interestingly, phylogenetic conservatism was substantially weaker in Polynesia, and this coincided with longer food chains, wider resource breadth, and broader niche widths (Fig.
3A ). Further, in accordance with N being more limiting in Mo'orea, N excretion rate was significantly lower in Mo'orea, indicating that due to the high demand of N, these fishes were assimilating greater amounts of N from their diets (body N was also absolutely lower in Mo'orea but not beyond that predicted by phylogenetic relatedness). Importantly, we also show that the cross-region differences in chemical traits cannot be explained by trait differences within families found in both regions (e. g. , Lutjanidae species in the Caribbean versus Polynesia). This, taken with consistent findings that families explain the most variation in chemical traits  19, 22, 23, 37, 38  , suggests that regional differences may be driven by families unique to each region. We acknowledge that we cannot speak to within-region biogeographic patterns because our data were collected from specific locations (one island in each region) as opposed to more broadly across these regions.
To estimate phylogenetic relatedness, we used a time tree that only included species placed with genetic data 54 obtained with the "fishtree" package in R  55. The phylogeny was made ultrametric using non-negative least squares  56  and then was converted into an inverted phylogenetic covariance matrix using the algorithm from  57  implemented with the "inverseA" function from the MCMCglmm package in R  58. This covariance matrix was then incorporated as a random effect in the Bayesian phylogenetic mixed model. Models were run in R using the "MCMCGlmm" package  58. We used uninformative priors and ran the model for 80,000 iterations with a burn-in of 10000. Model convergence was assessed by inspecting trace plots. We quantified the total variation in the data explained by (1) a "full model" that included all fixed effects (ecological factors) plus a random intercept (species-level effects) from which we partitioned the amount of variance explained by the ecological factors alone and the species-level random effects (phylogenetic effects), and (2) the "phylogeny-only", or intercept-only model, that only included the phylogenetic covariance error structure following  59.
To compare models, we quantified DIC for each model  60. How does the relative strength of phylogenetic conservatism differ between the Caribbean and Polynesia? The strength of phylogenetic conservatism, or the phylogenetic signal, was determined by calculating Pagel's Lambda and Bloomberg's K using the phylosig function in the "picante" package in R, including the randomization test for the statistical difference using 999 simulations  61. Calculating these values requires species-level means. Given the wide range of individual data points we have per species (1-115 per species), we used a bootstrapping procedure whereby we iteratively resampled (creating each time resampled data of equal size to the observed data) with replacement from each region and the full dataset to calculate the species-level mean 1000 times, each time recalculating Pagel's Lambda and Bloomberg's K based on this resampled estimate.
Respectively (1) Trait ~1 + family:region (2) Trait ~1 + family:region + 1 | sp + 1 | phylo Models 1 and 2 were run using "MCMCglmm" package in R  58  , using uninformative priors and for 80,000 iterations with a burn-in of 10,000. Model convergence was assessed by inspecting trace plots.