dataesr/scientific-paragraphs-categorization huggingface dataset:
While PROVOR and SOLO floats are designed to self-correct any pressure drift, APEX floats do not make any internal pressure correction as they return "raw" pressures. For this latter float model, adjustments are applied both in real time (by the DACs) and delayed mode (by the PIs) by using the surface pressure values returned by the float (surface pressure is measured while the float surfaces and it is assumed to be zero if no sensor drift exists). However, delayed mode processing is a long-term task and some floats have not yet been reprocessed. Some simple diagnostics on the state of corrections for Argo data in the CORA3 dataset are given in Sect. 4. 1. 2.
It was also suggested early on that the assumption of a terminal velocity might not be always correct, in particular in the surface layer, and, compounded with time constant issues, can result in a depth offset (although the determination of this depth offset is not straightforward, as discussed by di Nezio and Goni, 2011). Moreover, the weight and hydrodynamic characteristics of the probe and/or wire are known to strongly influence the fall-rate equation. Seaver and Kuleshov (1982) , for example, indicate that a weight uncertainty of 2 % could induce an 8. 8 m depth error at 750 m. A variety of approaches have been used to correct these biases (e. g. Wijffels et al. , 2008; Levitus et al. , 2009; Ishii and Kimoto, 2009; Gouretski and Reseghetti, 2010; Hamon et al. , 2012). The correction applied on the CORA3 dataset is an application of the statistical method described in  Hamon et al.
(2012). This correction is based on the comparison of XBT profiles with co-localized reference profiles (CTD). The correction is calculated for each year and divided into two parts: first the computation of a depth-independent temperature correction (temperature offset) based on comparisons with reference profiles in the near surface layer and then a correction of the depth with a second order polynomial function. For further details of this method, the reader should refer to  Hamon et al. (2012). Rather than use the coefficients given in Table  2  of  Hamon et al. (2012) , for several reasons, we recompute them. First, the coefficients given in  Hamon et al. (2012)  only go up to 2007, while we also need them for the last 3 yr of the CORA3 dataset  (2008) (2009) (2010). Second, looking at Fig.
We thus chose not to apply the Hanawa fall rate for XBT depth computed with the old fall-rate equation  (Hanawa et al. , 1995). This differs from  Hamon et al. (2012) , where the linear Hanawa correction was first applied when possible. The coefficients computed in our case slightly differ from those given in  Hamon et al. (2012) , because they are computed with different reference profiles and because they compensate for the fact that we did not apply the Hanawa correction for a part of XBT profiles. In the CORA3 dataset, we applied the correction based on the  Hamon et al. (2012)  method for all the XBT profiles in XB, BA and TE files with an instrument type that refers to an XBT probe (see www. nodc. noaa. gov/GTSPP/document/ codetbls/gtsppcode. html). Profiles in XB files with an unknown instrument type and no salinity data (to avoid XCTD) were also considered as XBT.
Overview Figure  10  shows the percentage of profiles in the CORA3 dataset that have bad quality flags (flags 3 or 4) for position, date or for at least 75 % of temperature or salinity measurements. To produce these statistics, the best profile available is used, meaning that if temperature or salinity has been corrected in delayed mode (for Argo and XBT data), then the adjusted profile and associated quality flags are taken into account instead of the raw profile. As shown in Fig. 10 , the percentage of profiles with a bad position in the CORA3 dataset is highly variable from one year to another. For 1999, the percentage of profiles with a bad position is close to 8 % in the dataset, mainly due to incorrect positions of TAO/TRITON moorings. To date, we have no explanation for this. It should be noted that TAO/TRITON, PIRATA and RAMA data in the CORA3 dataset are those received in real time from PMEL and GTS.
After 2003, a large proportion of salinity profiles were obtained from Argo floats. The percentage of bad salinity profiles among Argo floats is quite high, mainly because of a problem encountered with one float type (see the next subsection for more details). However, the percentage of bad salinity profiles among Argo floats started to be lower after 2007, once the problem was discovered and resolved. In the CORA3 dataset, the percentage of profiles with bad salinity data is still high after 2007 mainly because of data from coastal moorings. The quality of these high frequency moorings is difficult to evaluate with the tools we developed, as many of them are located in areas influenced by tides or estuarine processes and thus salinity measurements are very different from open-ocean climatology.
The special case of Argo floats Since the beginning of the Argo program, several data problems have been identified and corrections have been made or are in progress in each DAC. As a consequence, the Argo database is constantly evolving -even for the data acquired some years ago -as some floats can be reprocessed a long time after data acquisition (1-2 yr on average). The most upto-date Argo database is on the GDAC ftp servers. Therefore, it is important to have in mind that, for Argo data, the CORA3 dataset reflects the status of the Argo database on the GDAC ftp servers at the date of data retrievals (mid-2010 for data that span 1990-2009 and March 2011 for the year 2010). These data were rechecked during the validation phase of CORA3 (as described in Sect. 3) to improve the data quality in a homogeneous way, but no supplementary data correction was applied.
Some simple diagnostics about Argo data quality and state of corrections in the CORA3 dataset are therefore highly necessary. For the CORA3 dataset, the adjusted parameters for Argo data are those received at the GDAC at the date of the retrieval. In the CORA3 dataset, about 75 % of Argo float profiles are adjusted for pressure and/or salinity (63 % in delayed mode and 12 % in real time, automatically). Figure  11  shows the percentage of Argo profiles that have a bad quality flag either for position, date or at least 75 % of temperature or salinity measurements. The different colours indicate the different float models (mainly APEX, SOLO and PROVOR). Most of the floats are fitted with SBE (Seabird Electronics, Inc. ) CTD sensors, while a smaller number, deployed mostly by WHOI, are fitted with FSI (Falmouth Scientific Instruments) sensors.
In the aggregate, the WHOI FSI floats resulted in a cold bias. The problem was identified and, by the end of 2007, corrections were put on the GDACs for some of these floats (39), while the uncorrectable floats (165) were grey-listed (i. e. pressure measurements flagged as bad data). We checked that the grey list was applied to the CORA3 dataset. Finally, about 87 % of the profiles from SOLO floats with FSI sensors are unusable in the CORA3 dataset because the position, the date, the pressures or the temperatures have been flagged as bad. There have been various other issues with the pressure measurements, reported at different times. By mid-2002, SBE had chosen to install pressure sensors from Druck Corporation in all of their CTDs, mainly because of their stability. However, in early 2009, a problem was found with the Druck pressure sensor.
, 2012) , we could identify 100 955 profiles with TNPD (about 20 % of all APEX floats profiles) in the CORA3 dataset. We did not perform specific quality checks for these profiles, but a fraction of them has been already flagged as bad thanks to the previous tests: among all the profiles we identified as TNPD, about 13 % are flagged as bad either for pressure, temperature or salinity. However, only severe negative pressure drifts show observable temperature and salinity anomalies (an error of -20 dbar would cause a positive salinity error of approximately 0. 01 PSS-78). Barker et al. (2011)  estimated a median error of -3 dbar for all TNPD profiles that can be compared with a close good profile. As a consequence they recommended that all the TNPD floats be excluded from studies of oceanic heat content and decadal changes.
In the CORA3 dataset 13 % of TNPD floats are flagged as 4, meaning that probably only the most severe negative drifts have been caught. The list of TNPD floats identified in the CORA3 dataset is provided, along with the CORA3 files. Figure  12  illustrates the state of correction for APEX float profiles that are correctable in CORA3 (not TNPD and with sufficient information and surface pressure data). Among them, about 27 % are not corrected and 23 % have a correction equal to zero. In the latter case, this could be because the float does not need any pressure correction or, more probably, because the float has been processed in delayed mode but only for the salinity parameter. The geographical distribution of the corrections can be compared to the Fig. 5  of  Barker et al. (2011). In CORA3, more profiles are corrected for a pressure drift than in the GDAC Argo dataset as of January 2009.
Von Schuckman and Le Traon (2011) have proposed a method to evaluate GSSL from irregularly distributed profiles. They divide the ocean into boxes of 5 • latitude, 10 • longitude and 3 month size. The mean for each box is then estimated using a weighted averaging method based on the analysis of  Bretherton et al. (1976). In their study, von Schuckman and Le Traon only used the Argo dataset downloaded from the Coriolis data centre and rechecked it to reach the quality level required by climate change studies. In this paper we used their box averaging method to evaluate GSSL with the CORA3 dataset (using flag 1 and 2 only, i. e. good or probably good data) for the time period 2005-2010, i. e. a time period where global coverage is guaranteed mainly due to the global Argo observation array. Results obtained with the CORA3 dataset are compared to those of von  Schuckmann and Le Traon (2011)  in Fig.
13. Using the CORA3 dataset, the 6-year GSSL trend is 0. 64 ± 0. 12 mm yr -1 (or 0. 58 ± 0. 10 mm yr -1 keeping only Argo data) and lies within the error bars of the von Schuckmann and Le Traon (2011) estimates. Although encouraging, there can be several explanations for this quite good agreement. One of the reasons is that the method used to compute the GSSL is robust and not very sensitive to any bad data that possibly remains in our dataset. Another reason is the possible compensatory effect in our GSSL estimate of some residual positive and negative biases. For example,  Barker et al. (2011)  noted that negative biases from uncorrectable (and other unusable) APEX profiles nearly compensate positive biases from correctable (but not yet corrected) APEX profiles in the global 0-700 m thermosteric sea level.
Conclusion and directions for future work This paper was intended to present the CORA3 dataset, its links with the Coriolis database (which conditions the data sources as well as real-and near-real-time quality controls) and the supplementary validation procedure applied to recheck the CORA3 dataset as a whole. This validation step relies on statistical tests designed to isolate suspicious profiles. Such profiles are then visually checked and quality flags are modified if judged necessary. No validation system is perfect, and it was necessary to deal with the number of suspicious profiles scrutinized, as this can rapidly become timeconsuming. However, human intervention was found to be required both to avoid rejecting to much good data or leaving some gross errors. When checking a large amount of profiles over the global ocean, there is the possibility that we flagged an observation as bad whereas a regional expert would have left it as good or vice-versa.
Our general approach was not to flag a measurement as bad if we had some doubts, meaning that if the visual checks performed on the profile (comparison to climatology and neighbouring profiles) were not sufficient to decide if it was good or not, we left the flags unchanged. In the same way, quality flags of Argo profiles already processed in delayed mode by the PIs were generally not modified except if an error was obvious. Statistical tests could also be improved, especially in some regions (e. g. Southern Ocean) or for certain types of data (e. g. coastal moorings). Background quality control based on the global ocean reanalysis GLORYS2V1 was implemented for this version of CORA. It proved to be a powerful tool to improve the quality of in-situ observation datasets. It also highlighted the mutual benefits that data centres and operational forecasting centres can have when working closely together: improvement of delayed time observation datasets and consequent C.
Cabanes et al. : The CORA dataset improvements in ocean reanalysis quality. Such feedback from modellers will be sought in the future, particularly in the framework of the MyOceanII project. The delivery of a global dataset such as CORA3 should be accompanied by supporting documentation sufficient to allow different types of users to evaluate if the dataset can meet their own needs (those needs can differ if they intend to use the database, for example, in global reanalysis projects, to study a specific region or to monitor global oceanic changes). For this purpose, we developed a series of simple diagnostics to monitor data quantity and coverage and data quality. In terms of data quantity, a better coverage of the European seas will be done in partnership with the MyOceanII in-situ thematic assembly centre partners and the SeaDataNetII FP7 project.
In terms of data quality, it appears to us that it is crucial to deliver sufficient information to help users evaluate the state of corrections for known instrumental biases, drifts or problems in the dataset. In this paper, we have mainly focussed on known biases or problems for Argo floats for which corrections have been made or are in progress. Future versions of CORA will include more data reprocessed in delayed mode by the originators (e. g. TAO/TRITON, PIRATA and RAMA moorings or sea mammal data). The use of GOIs such as GSSL to evaluate the quality of a global dataset is interesting. In our case, this allows us to check the efficiency of our validation procedure compared to the one used in von Schuckmann and Le Traon (2011). However, this does not exclude the possibility that there are still unknown drifts and/or biases present in the data.
(Wong et al. , 2012)lied (in the order given) to the profiles downloaded into the Coriolis database. Unless stated otherwise, one could refer to the Argo Quality Control Manual V2. 7(Wong et al. , 2012)for a complete description of these tests. Some of the automatic tests are only specific to Argo floats (e. g. digit rollover test) and are not applied to the other data types.
Acknowledgements. It would have not been possible to prepare the CORA3 dataset without all those working at the Coriolis data centre who collect, decode, quality control and distribute the data, and maintain the Coriolis database. The research and operational programs that make oceanic observations are also fundamental. Argo data were collected and made freely available by the  International Argo Project  and the national programmes that contribute to it (http://www. argo. ucsd. edu,http://argo. jcommops. org). Argo is a pilot programme of the Global Ocean Observing System. The authors would like to thank the three anonymous reviewers whose comments greatly improved the manuscript. The authors were funded by  CNRS ,  IFREMER ,  Coriolis ,  Mercator Ocean  and the  European Community Seventh Framework Program   FP7/2007-2013  under grant agreement no.
New ingredients with respect to our previous analysis  [39]  are the following. • We use latest data from the MiniBooNE ( -) ν µ → ( -) ν e appearance searches  [15] [16] [17]. Our MiniBooNE appearance analysis is now based on Monte Carlo events provided by the collaboration taking into account realistic event reconstruction, correlation matrices, as well as oscillations of various background components in a consistent way. • We include the constraints on the appearance probability from E776  [40]  and ICARUS  [41]. • We include the Gallium anomaly in our fit. • We take into account constraints from solar neutrinos, the KamLAND reactor experiment, and LSND and KARMEN measurements of the reaction ν e + 12 C → e -+ 12 N. • The treatment of the reactor anomaly is improved and updated by taking into account small changes in the predicted anti-neutrino fluxes as well as an improved consideration of systematic errors and their correlations.
(2. 5) Note that in the numerical analysis of MINOS data neither the SBL nor the LBL approximations can be used because φ 31 , φ 41 and φ 51 can all become of order one either at the far detector or at the near detector  [65]. Moreover, matter effects cannot be neglected in MINOS. All of these effects are properly included in our numerical analysis of the MINOS experiment. Sometimes it is convenient to complete the 3 × (3 + s) rectangular mixing matrix by s rows to an n × n unitary matrix, with n = 3 + s. For n = 5 we use the following parametrization for U : U = V 35 O 34 V 25 V 24 O 23 O 15 O 14 V 13 V 12 (2. 6) where O ij represents a real rotation matrix by an angle θ ij in the ij plane, and V ij represents a complex rotation by an angle θ ij and a phase ϕ ij. The particular ordering of the rotation matrices is an arbitrary convention which, however, turns out to be convenient for practical reasons.
SBL reactor experiments The data from reactor experiments used in our analysis are summarized in Tab. 3. Our simulations make use of a dedicated reactor code based on previous publications, see e. g. [79, 80]. We have updated the code to include the latest data and improved the treatment of uncertainties, see appendix B for details. The code used here is very similar to the one from Ref. [11] , extended to sterile neutrino oscillations. [75]  820 1 rate Chooz  [76]  1050 14 bins DoubleChooz  [10]  1050 18 bins DayaBay  [77]  6 rates -1 norm RENO  [9]  2 rates -1 norm KamLAND  [78]  17 bins Table  3. Reactor data used in our analysis. The experiments in the upper part of the table have baselines L < 100 m and are referred to as SBL reactor experiments. For these experiments we list the baseline, the ratio of the observed and predicted rates (based on the flux predictions from  [20, 21] ), the uncorrelated error, and the total experimental error (i.
2. Consistent with the above discussion we find mixing angles somewhat smaller than those obtained by the authors of  [19]. The best fit point from combined Gallium+SBL reactor data is given in Tab. 4, and the no-oscillation hypothesis is disfavored at 99. 9% CL (2 dof) or 3. 3σ compared to the 3+1 best fit point. Let us consider now the Gallium and SBL reactor data in the framework of two sterile neutrinos, in particular in the 3+2 scheme. SBL ν e and νe disappearance data depend on 4 parameters in this case, ∆m 2  41 , ∆m 2  51 , and the two mixing angles θ 14 and θ 15 (or, equivalently, the moduli of the two matrix elements U e4 and U e5 ). We report the best fit points from SBL reactor data and from SBL reactor data combined with the Gallium source data in Tab. 5. For these two cases we find an improvement of 5.
Global data on ν e and νe disappearance Let us now consider the global picture regarding ( -) ν e disappearance. In addition to the short-baseline reactor and Gallium data discussed above, we now add data from the following experiments: • The remaining reactor experiments at a long baseline ("LBL reactors") and the very long-baseline reactor experiment KamLAND, see table  3. • Global data on solar neutrinos, see appendix C for details. • LSND and KARMEN measurements of the reaction ν e + 12 C → e -+ 12 N  [91, 92]. The experiments have found agreement with the expected cross section  [93] , hence their measurements constrain the disappearance of ν e with eV-scale mass-squared differences  [94, 95]. Details on our analysis of the 12 C scattering data are given in appendix E. 1. So far the LBL experiments DayaBay and RENO have released only data on the relative comparison of near (L ∼ 400 m) and far (L ∼ 1.
[100] , which finds that the significance of the reactor anomaly is reduced to 1. 4σ when LBL data and a finite value of θ 13 is taken into account. Solar neutrinos are also sensitive to sterile neutrino mixing (see e. g. [101] [102] [103] ). The main effect of the presence of ν e mixing with eV states is an over-all flux reduction. While this effect is largely degenerate with θ 13 , a non-trivial bound is obtained in the combination with DayaBay, RENO and KamLAND. KamLAND is sensitive to oscillations driven by ∆m 2  21 and θ 12 , whereas sterile neutrinos affect the overall normalization, degenerate with θ 13. The matter effect in the sun as well as SNO NC data provide additional signatures of sterile neutrinos, beyond an overall normalization. As we will show in Sec. 4 solar data depend also on the mixing angles θ 24 and θ 34 , controlling the fraction of ν e → ν s transitions, see e.
For our MiniBooNE ν µ disappearance analysis, we use the neutrino mode data from  [44]. As in appearance mode, we compute the expected event spectra for each parameter point by using MiniBooNE's Monte Carlo events. Since backgrounds are very small for this analysis, we do not need to take them into account. For each set of oscillation parameters, we choose the overall normalization of the spectrum in such a way that the total predicted number of events matches the number of observed events in MiniBooNE, i. e. , we fit only the event spectrum, not the normalization. The log-likelihood is obtained in analogy to Eq. (E. 1) and thus takes into account systematic uncertainties and correlations between different energy bins. In the analysis of νµ disappearance data, we follow the combined MiniBooNE/SciBooNE analysis from  [45].
We emulate the baseline uncertainty due to the non-vanishing length of the MINOS decay pipe by smearing the oscillation probabilities with an additional Gaussian of width σ2 = (2. 0 GeV-3. 0×E true ) 2 and setting the effective distance between near detector and neutrino source to 700 m. This value as well as the parameters of the smearing function have been obtained numerically from the requirement that 2-flavor oscillation probabilities at the near detector computed with a more accurate treatment of the decay pipe are well reproduced for various mass squared differences of order eV. We compute neutrino oscillations in MINOS numerically using a full 4-or 5-flavor code that includes all relevant mixing parameters as well as CC and NC matter effects. In the fit, we predict the expected number of events at the far detector for a given set of oscillation parameters by multiplying the observed number of near detector events in each energy bin with the simulated ratio of far and near detector events in that bin.
Acknowledgments Numerical results presented in this paper have been obtained on computing infrastructure provided by  Fermi National Accelerator Laboratory  and by  Max Planck Institut für Kernphysik. The authors would like to thank the MINOS collaboration for their invaluable help in including their sterile neutrino search in this work. We are especially grateful to  Alexandre Sousa  and  Mary Bishai  for sharing their Monte Carlo results. We are grateful to  M. Smy  for providing assistance on the simulation of SK4 solar data, and to  Bill Louis  for valuable information on the MiniBooNE analysis. Fermilab is operated by  Fermi Research Alliance  under contract  DE-AC02-07CH11359  with the  US Department of Energy. P. A. N. M. was supported by the  Fundação de Amparo à Pesquisa do Estado de São Paulo.