dataesr/scientific-paragraphs-categorization huggingface dataset:
Management Present day stand densities were available for all sites from the CEIP and other project databases such as FLUXNET and complemented with data from publications (Table  2 ). However, a reasonably complete time-series of planting densities and stand thinning events from planting were available for only a few sites. Where planting density was unavailable, we assumed a default initial value of 4500 trees ha -1. For sites with missing thinning histories, we created plausible thinning histories using stand density and thinning data where available and filling in the gaps by loosely following the principle in  Cameron et al. (2013)  that after an initial thinning of 40% at 20 years there would be decadal thinning of 20%. A figure showing the stand history reconstructions used is given in the supplementary material.
) with a brief summary presented here. The dry deposition fraction of the total N deposition was estimated from concentrations using an ensemble average of four different deposition models  (Flechard et al. , 2011). Wet deposition data came from three sources. Firstly a chemical analysis of precipitation at each of the sites. Secondly through spatial interpolation (kriging) of data from the wet deposition monitoring network in Europe to the sites. Finally wet deposition was calculated by the 50x50 km EMEP model. The three data sources were combined by taking the arithmetic mean. Wet and dry deposition was then added together to give total N deposition values.
Prior parameter minimum, mode and maximum The prior values for the generic parameters are listed in the supplementary material. The same prior was used for pine and spruce. Initial soil litter and organic matter carbon had the same prior within each category of pine, spruce and deciduous forests. Where possible, parameter priors were estimated from measurements at sites or from literature. In many cases this was not possible as either data were not available or the model parameter had a different role in the model than that measured. In these cases a wide prior was used. Values for these wide priors were guided by previous studies with BASFOR  (Van Oijen et al. , 2005; Van Oijen and Thomson, 2010; Van Oijen et al. , 2011).
Soil depth and water retention parameters Prior ranges for soil water retention and soil depth parameters were chosen on a site by site basis using site observations as described in section 2. 1. 3. These data were used to inform the modes of the soil water retention and rooting depth parameters at each site. Prior uncertainty was set at 20% of the mode value for measured quantities and 40% for estimated quantities. The soil water saturation parameter was assigned a prior uncertainty of 30% of the mode value. Tables showing the prior ranges for each site are given in the supplementary material.
Quantifying the influence of the calibration on model-data differences and uncertainty We used two main measures to quantify how the model had changed after calibration. The first quantified how the model-data difference had changed as a result of the calibration. While neither model nor data are without error, a closer match after calibration is generally indicative of an improvement in model performance. Model-data differences were quantified using the root mean square (RMS) difference of the observations and the model run with the maximum a-posteriori (MAP) parameter vector, since this was the most probable model prediction given the data, and prior mode parameter vectors as this represents the most probable parameter vector prior to the comparison against data. The ratio of MAP to prior mode RMS deviations was used to quantify how model-data differences had changed after calibration.
Changes in model outputs after multi-site calibration Before considering how multi-site calibration differs from calibrating at each forest site separately and which observations were most effective in the calibration, we first present a summary of the differences the multi-site calibration made to the model outputs. We use the method of analysis reported in section (2. 6). We analyse and summarise how model-data differences changed after calibration by comparing model output against data from the most likely parameter vector prior to calibration (the prior mode) and the most likely parameter vector after calibration (the maximum a posteriori (MAP)) using root mean square (RMS) 10 Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-156 Manuscript under review for journal Biogeosciences Discussion started: 20 April 2018 c Author(s) 2018.
The influence of calibration on model -observation RMS deviations. We first consider which of the model outputs showed the largest change in RMS deviation from data after calibration. Of the low frequency model outputs, aboveground vegetation (CLBS), roots (CR), DBH and LAI generally show the largest reductions in model data differences after calibration. Whereas height (H) and soil organic matter carbon (CSOM) show the largest increases in model-data differences. Model-data differences in height were generally smallest prior to the calibration which partially helps to explain the lack of improvement after calibration. For the low frequency variables, model outputs at sites NL-Spe and IT-Ren generally moved furthest away from the observations, relative to the prior mode, after BC, although IT-Ren was one of the sites closest to the observations prior to the BC.
For the high frequency variables, model outputs, soil water content (WC) was closest and GPP furthest away from observations before calibration. WC also showed the greatest relative reduction in RMS deviation after calibration whereas ET showed the greatest relative increase in RMS. In general, there was a more consistent relative reduction in RMS deviation after calibration than for the low frequency outputs which might be expected given the larger quantities of flux and WC data included in the calibration. For the high frequency variables, the forest sites that showed the largest relative reduction in model-data differences after BC were FI-Hyy, DK-Sor and DE-Hai. Sites that showed least reduction or even increases in relative RMS deviation after BC, included NL-Loo, SE-SK2, ES-ES1, IT-Ren and RU-Fyo.
How different sets of observations influence the calibration of BASFOR Since we have available a rich dataset encompassing many aspects of the forest ecosystem we evaluate how the inclusion of different sets of observations influences the calibration of BASFOR. For each of pine, spruce and deciduous forests six calibrations described in section (2. 5. 2) are run. The results presented below are averaged across all the forest sites and hence also across pine, spruce and deciduous forests. As previously, the results focus on how the model comparison against observations and how uncertainty has changed from prior to posterior (section 2. 6).
Comparison against high frequency data. As might be expected, the largest improvement in the comparison between model and observations occurs when the calibration includes the data for the variable being compared. For example, RMS deviations decrease more for NEE when NEE is included in the calibration (Fig. 5 ). Model derived GPP was not included in the calibration so in this case the largest decreases are found when all the calibration data are included although as might be expected inclusion of NEE is the next most important dataset for decreasing model and data differences. Not all the calibrations improved the comparison against observations. Indeed, the "ET" calibration increases model differences against the NEE and GPP data and similarly the "NEE" calibration slightly increases differences with the ET data.
Influence of pooled versus unpooled calibration on model -data RMS deviations. Shown in Fig. 9  are results for outputs where more data were included in the calibration (Table  3 ). Unpooled calibrations show greater improvement than pooled calibrations for NEE. Outputs GPP and ET show no great difference with WC showing a slightly greater improvement for the pooled calibration. For the outputs which generally have fewer observations included in the calibration (Fig. 10  and Table  3 ) CLBS, H and LAI have greater improvements for the unpooled calibration whereas CR, CLITT and CSOM show greater improvements for pooled. Consulting Table  3  this suggests that outputs with very few observations tend to benefit more from the pooled calibrations where observations from all the pine sites can influence the calibration.
How does a rich observational dataset benefit forest process-based model calibration? Access to good quality observations that represent more than just a few of the ecosystem variables under study, for calibrating PBMs, remains a significant challenge. While there are now increasing numbers of studies where ecosystem and even forest PBMs are calibrated at multiple sites, most studies to date have had access to data limited to just one or two aspects of the ecosystem. For example,  Lehuger et al. (2009)   2015 ) had only carbon flux data. In this study, we had access to observations, for calibration, on a significantly greater number of European forest ecosystem variables and at more forest sites than previous studies. The results demonstrate that while most model outputs had a closer fit to data after calibration this was not always the case.
Some model outputs, for example height, were relatively close to the data prior to the calibration making further improvements difficult. For other quantities, such as soil organic matter, there were very few observations and the uncertainty was set high, reflecting a lack of confidence in the accuracy of the data, hence, this data had relatively little impact on the calibration. In general, there were more consistent reductions in model-data differences where a greater number of calibration data, such as NEE or soil water content observations, were available. In contrast, evapotranspiration, for which there were also a relatively large number of data points, had increased RMS differences after calibration, at some sites. Such increases in a-posteriori model-data differences could suggest inconsistencies between the data and the model, so that improvements in the model representation of some parts of the ecosystem come at the expense of others.
We will return to this subject when discussing the results of calibrations with different datasets below. There was considerable variation in the extent to which model simulations improved after calibration at the different forest sites. This would indicate either a variation in the accuracy of forest data from different sites or a variation in the ability of the model to represent some of the sites which may be atypical of European forests in general or both. We will discuss this further in the next section when comparing pooled and unpooled calibrations. While it is often of primary concern whether the model fit to data improved after calibration, it is difficult to assess the value of model predictions without making an assessment of uncertainty. Since we use mathematical probabilities to combine models and data, we can quantify uncertainty propagated from the parameters through to model predictions that have a precise meaning mathematically, aiding interpretation.
There was a consistent reduction in uncertainty after calibration with a strong relationship between the number of data-points included in the calibration and uncertainty reduction. Uncertainty only increased where the range of prior uncertainty was constrained, due to prior model values being close to zero for a field such as LAI that cannot be negative. A number of findings can be taken from the results. Most obviously, reducing model-data differences is most effective when the data used in the comparison are also included in the calibration. Secondly, the single dataset calibrations highlighted where there were inconsistencies either between the different observational datasets or between the observations and the model or both (subsequently referred to in the text as just "inconsistencies").
The single dataset calibrations also shed light on how these inconsistencies had influenced the calibration with all the observations included. For example, a calibration including just NEE observations increased model-data evapotranspiration differences and vice versa. The "NEE" calibrations had a tendency to give higher aboveground carbon accumulations than were expected from the observations whereas the "ET" calibration had a tendency to give lower aboveground carbon accumulations than those expected from observations. This indicates that there could be inconsistencies between the observations of NEE, evapotranspiration and aboveground-carbon and the model. These inconsistencies hampered reductions in model-data differences in these outputs when all the calibrations data were included. The results from the calibrations cannot help to disentangle the source of the issue but they can help to diagnose where there may be conflicts between the model and the data and also between datasets.
Where the calibrations showed smaller inconsistencies, such as for the NEE and soil water content only calibrations and root carbon, DBH and LAI data, there were greater reductions in model-data differences for root carbon, DBH and LAI in the calibration with all the data included. Similarly, while NEE and evapotranspiration (ET) had approximately equal numbers of observations and accuracy, NEE data were more influential than ET data in the calibration when all the data were included. The evapotranspiration only calibration had larger model-data differences with the carbon stock data than the NEE only calibration. This may help to explain why the model was generally closer to NEE data but further from the evapotranspiration observations at some sites after calibration, as already noted. In general, calibration data was less effective in reducing model-data differences when there were larger inconsistencies present.
Further, the results indicated a relationship between the size of the inconsistencies and the quantity of calibration data required to influence the calibration, when all the observations were present. Where calibrations that included just one datatype suggested that there was a relatively large increase in model-data differences after calibration (for example, NEE and evapotranspiration only calibrations against height observations and the NEE only calibration and soil organic carbon (CSOM) observations) this would suggest a larger inconsistency. For larger inconsistencies the data (height or CSOM) had to have relatively greater "weight" in the likelihood function, either through greater accuracy or a larger number of observations, to influence the calibration when all the data are included in the calibration.
How effective are different observational datasets for reducing uncertainty in BC? As would be expected, the greatest reduction in uncertainty was generally when all the observations were included in the calibration. Uncertainty in model output is reduced the most when observed data representing that output is included in the calibration. Uncertainty was reduced more where there was a strong relationship between the model output and the data included in the calibration. For example, the second largest reduction in uncertainty in GPP and also larger reductions in carbon pools above and belowground was for the calibration that included just NEE observations. Indeed, the relatively sparse low frequency data had a disproportionately large influence on reducing uncertainty in the carbon stocks but also in the carbon fluxes relative to the much more numerous soil water content data.
Further, large reductions in uncertainty also occurred when the observations included in the calibration had a tendency to increase model-data differences. For the example, the inclusion of ET/NEE observations caused the third largest reduction in uncertainty in NEE/ET. Therefore, a large reduction of uncertainty in the calibration does not imply that the model has become much more accurate. While these reduced uncertainties are mathematically correct, if we assume that the model and data do not have structural errors and systematic biases, the small a-posteriori uncertainties are not useful if the models are to be used for predicting the future of European forests, with appropriate uncertainties quantified. To improve the utility of the uncertainties that are quantified, we need to represent uncertainty about model structural error and systematic observational biases in the Bayesian calibration.
, 2008; Lehuger et al. , 2009; Molina-Herrera et al. , 2015)  or not  (Van Oijen et al. , 2013). Since previous forest studies had access to more limited calibration data at multiple sites than here, Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-156 Manuscript under review for journal Biogeosciences Discussion started: 20 April 2018 c Author(s) 2018. CC BY 4. 0 License. we revisited the question of whether pooled or unpooled calibrations were more beneficial for reducing model-data differences and uncertainty, aiming to provide a more definitive answer than was possible before. We found a relationship between the quantity of data included in the calibration and whether a pooled or unpooled calibration was more beneficial for reducing model-data differences. In general, where more data were included, the unpooled calibration reduces RMS difference with data more than the pooled calibration whereas, if very few observations are available at a forest site, the pooling of observations from other forest sites was often more beneficial.
This is consistent with what was found previously because studies with relatively larger quantities of calibration data  (Reinds et al. , 2008; Lehuger et al. , 2009; Molina-Herrera et al. , 2015)  reported a benefit for unpooled calibrations whereas, those with fewer data  (Van Oijen et al. , 2013)  did not find an improvement. Pooled calibrations were more beneficial for sparse observations because the influence of low data quantities in the calibration increases when pooled with other sites. Therefore, pooled calibrations can help when datasets have large imbalances in the quantity of data available. Since it is likely that obtaining observations for some parts of the forest ecosystem, for example eddy-fluxes, will remain easier than for others, such as belowground carbon amounts, some pooling in calibrations will remain important.
In other words, calibrations which include data from just a few aspects of the ecosystem can be problematic since improved model fits for the parts of the ecosystem represented by the data can be at the expense of other parts of the ecosystem where the model-data fit can worsen. -The single dataset calibrations diagnosed where inclusion of data representing particular ecosystem variables led to significant increases or decreases in model-data differences in other ecosystem variables after calibration, suggesting a strong relationship between the underlying processes as represented in the model. -Where the single dataset calibrations led to an increase in model-data differences in other ecosystem variables after calibration this could be due to systematic errors in the data or structural errors in the model or both.
These errors hampered the reduction in model-data differences in the calibration with all the observations present. -Where the single-dataset calibrations diagnosed a larger increase in model-data differences in other ecosystem variables after calibration a larger quantity of the other ecosystem data was needed in the calibration to reduce model-data differences. -Where a stronger relationship between ecosystem variables was found there were also larger reductions in uncertainty after calibration. -For some ecosystem variables uncertainty reduced after calibration but model-data differences increased. This would suggest that there were deficiencies in the model or systematic errors in the data or both. -Sparse plant and soil stock observations were more important for reducing model-data differences and uncertainty in above and belowground carbon pools than more plentiful carbon and water flux data.
Acknowledgements. Figure 1. 1 Figure1. Timeseries of 30 day averaged carbon and water fluxes and soil water content for observations (blue line, with two standard deviations as used in the likelihood shaded blue) and the prior (the red line is the prior mode with the 5th to 95th quantile range shaded red) and posterior (the black line is the maximum a posteriori (MAP) with the 5th to 95th quantile range shaded) of BASFOR for Finnish site Hyytiälä.
■ ACKNOWLEDGMENTS M. P. d. L. -C. acknowledges the financial support from the Spanish Agencia Estatal de Investigacioń (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER, UE) under Grant No. MAT2016-75354-P as well as the computational resources provided by the CESGA Supercomputer Center (Spain). The aid of the COST Action CM1405 "Molecules in Motion" (MOLIM) is also well appreciated.
Deviation in initiation size Growth rate α (min -1 )  (A) Cell growth during the C + D period, quantified by the logarithmic ratio between the final and initial volume (y axis, binned averages), anticorrelates with cell size at initiation (x axis). The plot is centered by the mean values of the x-and y-axis variables to compare different datasets. Solid lines are linear fits. (B) The duration of the C + D period (y axis, binned averages) anticorrelates with the growth rate of individual cells (x axis), with a near-inverse pattern. (C) The correlation between size at initiation and growth during the C + D period (A) attains the observed intermediate slopes (gray shaded area) in the concurrent processes hypothesis (cyan lines; see Materials and methods for parameters), but not if one assumes that replication is always (orange line) or never limiting (purple line) cell division.
, B and D, listed in Materials and methods). (C and D) The negative correlation of interdivision time with individual cell growth rate [exemplified for one dataset in (C)] has a decreasing trend with increasing growth rate, captured by the model (cyan line; p H = 0. 25; other parameters were fixed as in the other figures; see Materials and methods). Cell cycle subperiod data are from  (6, 12). Interdivision cycle data [in (D)] are from  (6, 10, 19, 28). cycle. We can draw a comparison with the way the cell cycle is described in animal cells. A prevalent narrative of the cell cycle, particularly for (higher) eukaryotes, is a succession of well-determined transitions under a common master clock. However, this paradigm struggles to find the "final trigger" of cell cycle intervals  (26). Instead, in line with our concurrent processes view, emerging evidence supports the idea that a key aspect could be the integration of multiple decisions  (26, 27).
Data analysis Figures  2  and 3 (A  and C ) use data on SeqA foci formation from  (6, 12). The data contain information on cell division, size versus time, and replication initiation time of single tracked cells. Figure  3D  also uses datasets from  (6, 10, 19, 28). The data contain information on cell division and size versus time of single tracked cells for a total of about 10 6 cells. The dataset in (6) contains about 400 cells in the slow growth condition and about 90 pairs of mother-daughter cells in the intermediate growth condition. The dataset in (12) contains 80 cells. For the slow growth data in  (6)  and for  (12) , cells typically have a single DNA replication round during a cell division cycle, and motherdaughter tracking is not necessary. For the intermediate growth conditions of (6), cells typically have two overlapping rounds of DNA replication, and mother-daughter tracking is needed to define the C + D period, because it spans two cell cycles.