dataesr/scientific-paragraphs-categorization huggingface dataset:
Speleothem isotope data We use an updated SISAL database (SISALv1b:  Atsawawaranunt et al. , 2019) , which provides revised versions of 45 records from SISALv1 and includes 60 new records (Table  1 ). SISALv1b has isotope records from 455 speleothems from 211 cave sites distributed worldwide. Because the isotopic fractionation between water and CaCO 3 differs between calcite and aragonite, we use calcite speleothems, aragonite speleothems where the correction to calcite values was made by the original authors and speleothems with uncorrected aragonite mineralogy. We exclude speleothems where some samples are calcite and some aragonite (mixed mineralogy speleothems) and speleothems with unknown mineralogy. As a result of this screening, we use 407 speleothem records from 193 cave sites for comparisons.
The speleothem isotope ages were rounded to exact calendar years for this comparison. Data-model comparisons are generally made by comparing (1) anomalies between a palaeoclimate simulation and a control period with (2) data anomalies with respect to a modern baseline. There is no agreed standard defining the interval Table  1. List of speleothem records that have been added to SISALv1  (Atsawawaranunt et al. , 2018a, b)   Bartlein et al. , 2011). However, we also examined the impact of using shorter intervals for each time slice. We use the published age-depth models for each speleothem record. There is no information about the temporal uncertainties on individual isotope samples for most of the records in SISALv1b. This precludes a general assessment of the impact of temporal uncertainties on data-model comparisons.
Nevertheless, we assess these impacts for the LGM for two records (entity BT-2 from Botuverá cave:  Cruz et al. , 2005 ; and entity SSC01 from Gunung-buda cave:  Partin et al. , 2007)  for which new age-depth models have been prepared using COPRA  (Breitenbach et al. , 2012). We created 1000-member ensembles of the age-depth relationship using the original author's choice of radiometric dates and pchip (piecewise cubic hermite interpolating polynomial) interpolation. Isotope ratio means were calculated using time windows of increasing width (±100 to ±1500 years) around 21 kyr BP for the original age-depth model, the COPRA median age model and all ensemble members. All COPRAbased uncertainties have been projected to the chronological axes. To explore the use of absolute isotope data for model evaluation, we extracted absolute data for two transects illustrating key features of the geographic isotope patterns during the modern, MH and LGM periods.
We apply this approach at a global level to the unscreened SISAL data by counting the number of individual caves with stalagmite growth during every 1000-year period from 500 kyr BP to the present. Growth was indicated by a stable isotope sample at any point in each 1000-year bin, giving 3866 data points distributed in 500 bins. We use cave numbers, rather than the number of individual speleothems, to minimize the risk of over-sampled caves influencing the results. Random resampling (100 000) of the 3866 data points was used to derive 95 % and 5 % confidence intervals. The number of speleothems cannot be reliably predicted by a continuous distribution when numbers are low, so we do not consider intervals prior to 266 kyr BP -the most recent interval with less than four records.
Spatio-temporal coverage of speleothem records There are many regions of the world where the absence of carbonate lithologies means that there will never be speleothem records (Fig. 1a ). Nevertheless, SISALv1b represents a substantial improvement in spatial coverage compared to SISALv1, particularly for Australasia and Central and North America (Fig. 1a , Table  1 ), and the sampling for regions such as Europe and China is quite dense. Thus, SISALv1b provides a sufficient coverage to allow the data to be used for model evaluation. The temporal distribution of records is uneven, with only ca. 40 at 21 kyr increasing to > 100 records at 6 kyr and > 110 for the last 1000 yr (Fig. 1b ). A pronounced regional bias exists towards Europe during the Holocene. Regional coverage is relatively even during the LGM, except for Africa, which is under-represented throughout (< 4 % of total).
How well do the speleothem records represent modern δ 18 O in precipitation? The first-order spatial patterns shown by the SISAL speleothem records during the modern period ; n = 87) are in overall agreement with the OIPC dataset of interpolated wδ 18 O p (R 2 = 0. 76), with more negative values  at higher latitudes and in more continental climates (Fig. 3a ). The fact that the speleothem records reflect the δ 18 O patterns in modern precipitation confirms at a global scale the findings of  McDermott et al. (2011)  for the continental scale in Europe. There are no systematic biases between OIPC and SISAL data at different latitudes (Fig. 3b ). However, low latitude sites tend to show more positive δ 18 O values than simulated wδ 18 O p , whereas sites from middle to high latitudes tend to be more negative (Fig.
3c, d ). The discrepancies between the SISAL data and the observations or simulations may be due to cave-specific factors (such as a preferred seasonality of recharge, e. g. Bar-Matthews et al. , 1996 , or non-equilibrium fractionation processes during speleothem deposition, e. g. Ersek et al. , 2018) , by complex soil-atmosphere interactions affecting evapotranspiration (e. g. Denniston et al. , 1999)  and thus the isotopic signal of the effective recharge  (Baker et al. , 2019) , or uncertainties in the isotope fractionation factors with respect to temperature (Fig. S1  in the Supplement) amongst others (e. g. Hartmann and Baker, 2017). However, the overall level of agreement suggests that the SISAL data provide a good representation of the impacts of modern hydroclimatic processes. Comparison of the SISAL records with δ 18 O p weighted according to the potential recharge amount or with δ 18 O sw weighted to the moisture amount does not significantly improve the data-model comparison (Fig.
S2 ). The best relationship is obtained with soil water δ 18 O weighted according to soil moisture amount (wδ 18 O sw ; R 2 = 0. 76). However, smoothing the simulated wδ 18 O records on a sampleto-sample basis to account for multi-year transit times in the karst environment produces a slightly better geographic agreement with the SISAL records (Fig. S3 ). Accounting for differences between the model grid cell and cave elevations does not yield any overall improvement in the global correlations. Simulated inter-annual variability is less than that shown in the GNIP data (Fig. 4 ). Although there are missing values for the GNIP station data, we have also removed these intervals from the simulations, so incomplete sampling is unlikely to explain the difference between the observed and simulated inter-annual variability.
A relatively good agreement exists between the sign of the simulated and observed δ 18 O changes at the MH and LGM: 94 % of the MH entities and 84 % of the LGM entities show changes in the same direction after allowing for an uncertainty of ±0. 5 ‰ (Fig. 5a, b ). However, the magnitude of the changes is larger in the SISAL records than the simulations. The MH-modern speleothem anomalies range from -3. 60 ‰ to 1. 29 ‰ (mean ± SD: -0. 50 ± 1. 01 ‰), but the simulated anomalies only range from -0. 49 ‰ to 0. 28 ‰ (mean ± SD: 0. 00 ± 0. 32 ‰). Observed anomalies are 4-20 times larger than simulated anomalies in the Asian monsoon region, and in individual sites in North and South All SISAL isotope data have been converted to their drip-water equivalent, following the approach described in Sect.
, 2014) , particularly in monsoon regions (e. g. Perez-Sanz et al. , 2014). O p data for 1 to 16 years. On each box, the central mark indicates the median (q 2 ; 50th percentile) and the bottom and top edges of the box indicate the 25th (q 1 ) and 75th (q 3 ) percentiles, respectively. Outliers (black crosses) are locations with standard deviations greater than q 3 +1. 5× (q 3 -q 1 ) or less than q 1 -1. 5× (q 3 -q 1 ). This corresponds to approximately ±2. 7σ or 99. 3 % coverage if the data are normally distributed. If the notches in the box plots do not overlap, you can conclude, with 95 % confidence, that the true medians do differ. The grey horizontal band corresponds to the notch in SISAL for easy comparison. SISAL data were converted to their drip-water δ 18 O equivalent as described in Sect.
Although the use of shorter intervals is possible, we recommend using the conventional definitions of each time slice to facilitate comparison with other benchmark datasets. Although patterns in the isotope anomalies can provide a qualitative assessment of model performance, site-specific factors could lead to large differences from the simulations at individual locations. Improved spatial coverage would allow such sites to be identified and screened out before making quantitative comparisons of observed and simulated anomalies. Although there are only a limited number of records that cover both the modern baseline period and the MH (or the modern baseline period and the LGM), there are many more records that provide information about one or other of these periods. The examination of spatial gradients in absolute δ 18 O provides one way of exploiting this larger data coverage.
Even when an offset between the observed and simulated δ 18 O exists, comparing the trends along such gradients is possible. Thus, both absolute values and anomalies of the isotope data for data-model comparison are useful. Screening of published speleothem isotope data is essential to produce meaningful data-model comparisons. The SISAL database facilitates screening for mineralogy, which has a substantial effect on isotope values because of differ-ences in water-carbonate fractionation factors for aragonite or calcite that are more pronounced at lower temperatures (Fig. S1 ). Based on the limited number of records available at the LGM, speleothem age uncertainties have only a limited impact on mean isotope values, and propagation of such uncertainties as well as any model uncertainties would nevertheless substantially improve the robustness of data-model comparisons.
Based on our analyses, we therefore recommend that model evaluation using speleothem records should do the following: 1. filter speleothem records with respect to their mineralogy and use the appropriate equilibrium fractionation factor:  Tremaine et al. (2011)  for converting isotope data from either calcite or aragonite-corrected-to-calcite samples to their drip water equivalent, and  Grossman and Ku (1986)  as reformulated by  Lachniet (2015)  for converting isotope data from aragonite samples; 2. use the interval between 1850 and 1990 as the reference period for speleothem isotope records; 3. use speleothem isotope data averaged for the intervals 6000 ± 500 yr (21 000 ± 1000 yr) for comparability with other MH (LGM) palaeoclimate benchmark datasets; 4. use speleothem isotope data averaged for the interval 6000 ± 200 yr or 21 000 ± 500 yr for best approximation of midHolocene and lgm experiments; 5.
Conclusions Speleothem records show the same first-order spatial patterns as are available in the Global Network of Isotopes in Precipitation (GNIP) data and are therefore a good reflection of the δ 18 O patterns in modern precipitation. This observation suggests that stalagmites are a rich source of information for model evaluation. However, the inter-annual variability in the modern speleothem records is considerably reduced compared to the simulations, which in turn show less inter-annual variability than the GNIP observations. The low variability shown by the SISAL records -most likely from the low-pass filter effectively applied to the speleothem record by the karst system -precludes the use of this database for global studies focused on timescales shorter than quasi-decadal. Using the traditional anomaly approach to data-model comparisons, there is consistency between the sign of observed and simulated changes in both the MH and the LGM.
However, the ECHAM5-wiso model underestimates the changes in δ 18 O between time periods compared to the speleothem records (i. e. the amplitude of modelled δ 18 O changes is lower). Thus, these kinds of comparisons should only focus on the large-scale spatial patterns. Based on the available SISAL data, the use of smaller time windows than the conventional definitions for each time slice does not have a strong impact on the mean values and could be used to reduce the uncertainties associated with the palaeodata. However, this would preclude comparisons with existing benchmark datasets that use the conventional windows for the MH and LGM time slices. Only a limited number of speleothem records are continuous over long periods of time and the need to convert these to anomalies with respect to modern times is a drawback.
The limited number of records covering the LGM make the comparisons for this period particularly challenging. Nevertheless, continued expansion of the SISAL database will in-crease its usefulness for model evaluation in future. Furthermore, we have shown that alternative approaches using absolute values could help examine spatial trends and diagnose systematic offsets. Mismatches between simulations and observations can reflect the issues with the experimental design, problems with the model or uncertainties in the observations  (Harrison et al. , 2015). The failure to include changes in atmospheric dust loading, for example, has been put forward as an explanation of data-model mismatches in both the MH and the LGM (e. g. Hopcroft et al. , 2015; Messori et al. , 2019). Missing processes and feedbacks, such as climate-induced vegetation or land-surface changes, could also contribute to mismatches (e.
We have not screened for regionally anomalous records that could be influencing the results of our analyses, but this should certainly be done. Despite these challenges, SISAL appears to be an extremely useful tool for describing past patterns of variability, highlighting its potential for evaluating CMIP6-PMIP4 experiments. Comparisons with speleothem data can be seen as a complement to model evaluation using other types of palaeoenvironmental data and palaeoclimatic reconstructions (see for example  MARGO Project Members, 2009; Harrison et al. , 2014). They are particularly useful because they provide insights into how well state-of-the-art models reproduce the hydrological cycle and atmospheric circulation patterns. The ability to reproduce past observations provides additional confidence in the ability of climate models to simulate large climate changes, such as those expected by the end of the 21st century  (Braconnot et al.
, 2012; Schmidt et al. , 2014). However, mismatches between model simulations and palaeo-observations are also useful because they can help to pinpoint issues that may need to be addressed in developing improved models or in better experimental protocols  (Kageyama et al. , 2018) , providing that these mismatches do not arise because of misunderstanding or misinterpretation of the observations themselves. By providing a protocol for using speleothem data for data-model comparisons that accounts for uncertainties in the observations, we anticipate that at least such causes of data-model mismatches will be minimized. Data availability. The SISAL (Speleothem Isotopes Synthesis and AnaLysis Working Group) database version 1b is publicly available through the University of Reading repository at http: //doi.
org/10. 17864/1947. 189 (Atsawawaranunt et al. , 2019)  and through the NOAA's National Centers for Environmental Information (NCEI) at https://www. ncdc. noaa. gov/paleo-search/study/ 24070. The ECHAM5-wiso model output is available from https: //doi. org/10. 1594/PANGAEA. 902347 (Werner, 2019). The OIPC mean annual precipitation δ 18 O data are available from the Water Isotopes Database at http://wateriso. utah. edu/waterisotopes/ pages/data_access/ArcGrids. html (Bowen and Revenaugh, 2003; IAEA/WMO, 2015; Bowen, 2018). The Global Network of Isotopes in Precipitation (GNIP; IAEA/WMO, 2018) data from the International Atomic Energy Agency (IAEA) and the World Meteorological Organization (WMO) are available at https://nucleus. iaea. org/ wiser.
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-15-1557-2019-supplement. Team list. SISAL working group members who coordinated data gathering for the SISAL database are listed here in alphabetical order: Syed Masood Ahmad (Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India), Yassine Ait Brahim (Institute of Global Environmental Change, Xi'an Jiaotong University,  Xi'an, Shaanxi, China)  SISAL working members who submitted data to the SISAL database are listed here in alphabetical order: Tim Atkinson (Departments of Earth Sciences & Geography, University College London, WC1E 6BT, UK), Avner Ayalon (Geological Survey of  Israel, 32 Yesha'yahu Leibowitz, 9371234 , Jerusalem) , James Baldini (Department of Earth Sciences, Durham University, DH1 3LE, UK), Miryam  Bar-Matthews (Geological Survey of Israel, 32 Yesha'yahu Leibowitz, , 9371234 , Jerusalem) , Juan Pablo Bernal (Centro de Geociencias, Universidad Nacional Autónoma de México, Campus UNAM Juriquilla, Querétaro 76230, Querétaro, Mexico), Ronny Boch (Graz University of Technology, Institute of Applied Geosciences, Rechbauerstrasse 12, 8010 Graz, Austria), An-.
Outliers (black crosses) are locations with standard deviations greater than q 3 +1. 5× (q 3 -q 1 ) or less than q 1 -1. 5× (q 3 -q 1 ). This corresponds to approximately ±2. 7σ or 99. 3 % coverage if the data are normally distributed. If the notches in the box plots do not overlap, you can conclude, with 95 % confidence, that the true medians do differ. The grey horizontal band corresponds to the notch in SISAL for easy comparison. SISAL data were converted to their drip-water δ 18 O equivalent as described in Sect. 2. 3.
Acknowledgements. SISAL (Speleothem Isotopes Synthesis and Analysis ) is a working group of the Past Global Changes (PAGES) programme. We thank PAGES for their support for this activity. We thank the World Karst Aquifer Mapping project (WOKAM) team for providing us with the karst map presented in Fig. 1a  Review statement. This paper was edited by Zhengtang Guo and reviewed by three anonymous referees.
81 -46. 35 SBE3 Novello et al. (2018) São Matheus cave 631 -13. 81 -46. 35 SMT5 Novello et al. (2018) Shatuca cave 1960 -5. 7 -77. 9 Sha-2, Sha-3, Sha-composite Bustamante et al. (2016) Sofular cave 440 41. 42 31. 93 So-17A, So-2 Badertscher et al. (2011), Fleitmann et al. (2009), Göktürk et al. (2011) Soylegrotta cave 280 66 14 SG93 Lauritzen and Lundberg (1999) Tangga cave 600 -0. 36 100. 76 TA12-2 Wurtzel et al. (2018) Uluu-Too cave 1490 40. 4 72. 35 Uluu2 Wolff et al. (2017) White moon cave 170 37 -122. 183 WMC1 Oster et al. (2017) Xiangshui cave 380 25. 25 110. 92 X3 Cosford et al. (2008b) Xibalba cave 350 16. 5 -89 GU-Xi-1 Winter et al. (2015) Yaoba Don cave 420 28. 8 109. 83 YB Cosford et al. (2008b) used as a modern baseline for palaeoclimate reconstructions. fined as 6000±500 yr BP (where present is 1950 CE) and the Some studies have used modern observational datasets which LGM time slice as 21 000±1000 yr BP, following the conven- cover a specific and limited period of time and some use the tional definitions of these intervals used in the construction late 20th century as a reference.
We investigate the appro- of other benchmark palaeoclimate datasets (e. g. MARGO priate choice of modern baseline for the speleothem records Project Members, 2009; by comparing the interval centred on 1850 CE with alternative intervals covering the late 20th century, specifically1961-1990 and 1850-1990CE, and we assess the impact of these choices on both mean δ 18 O values and the number of records available for comparison. The MH time slice was de-.
Data collection Under Axshya SAMVAD study, we collected data for each study participant through record review (age, sex, ACF/PCF status, residence (urban/rural), distance of residence from microscopy centre, sputum smear grade, weight, diabetes status and HIV status) and patient interviews at their residence. Patient interviews were set up during the review of the participant's record. Before starting the patient interviews, we enquired about their past history of TB treatment for at least one month either from the public or private sector. Those with a past history of treatment were excluded from the Axshya SAMVAD study and referred to the programme for appropriate management. These constitute 'misclassification' for the purpose of present analysis.
Data analysis We double entered and validated the data using EpiData Entry software 9 (version 3. 1, EpiData Association, Odense Denmark). We analysed the data using STATA (version 12. 1, copyright 1985-2011 StataCorp LP USA)  10. We used frequency and proportions (0. 95 confidence intervals (CI)) to summarise (infer) the extent of misclassification. Adjusted analysis was done using log binomial regression to determine the factors associated with misclassification. Variables collected during record review (age, sex, ACF/PCF status, residence (urban/rural), distance of residence from microscopy centre and sputum smear grade) were included in the adjusted analysis. Baseline weight was missing in two-fifths of patients; baseline diabetes status was missing for more than three-fifths and HIV status was missing for two-fifths.
REVIEWER COMMENT The manuscript addresses one of the crucial problems with in India's Revised National TB Control Program. Interestingly, the study is based on our prior study (in which I was a lead author) in 2007, which was conducted in Mumbai and rural areas of Pune district. Given the research context and the data, I feel this manuscript should go as a brief communication or notes from the field rather than a full original research article. It does not make any novel contribution, but just confirms the earlier research finding. Even to make it a brief communication, I feel some points need to be provocatively addressed. My comments are as below. AUTHOR RESPONSE Thank you for the comment. F1000Research does not have the article types as suggested: brief communication or notes from the field.
We agree the findings are not novel, but are timely considering 10 years down the line, the issue remains as it is and this is important to highlight. Considering our manuscript is around 1800 words only, we are fine if F1000Research agrees to consider this as a research note. REVIEWER COMMENT Authors nowhere discussed the major implication of their observation that even after 10 years, there remains a big disconnect between the operational research and the program implementation, which is really unfortunate. This finding has another major implication that because of erroneous categorization, previously treated cases are being treated with first-line regimen, which results in amplification of resistance in those cases who may have primary or acquired drug resistance (from prior treatment) and/MDR-TB.
Acknowledgements The authors would like to acknowledge funding support for  Project Axshya  from  The Global Fund TB  grant to India. The Project is implemented by the Project Management Unit of The Union South East Asia Office since 2010 till date with support of the sub-recipient partners (in alphabetical order): The  Catholic Bishops' Conference of India-Coalition for AIDS and Related Diseases (CBCI-CARD) ; The  Catholic Health Association of India (CHAI) ;  Emmanuel Hospital Association (EHA) ;  MAMTA Health Institute for Mother and Child; Population Services International (PSI) ;  Resource Group for Education and Advocacy for Community Health (REACH) ; and  Voluntary Health Association of India (VHAI). Publication fee for this study were covered by the  Department for International Development (DFID) ,  UK  and  La Fondation Veuve Emile Metz-Tesch (Luxembourg).