dataesr/scientific-paragraphs-categorization huggingface dataset:
Stable isotope measurements Data were collected on two Thermo Finnigan MAT 253 gas source mass spectrometers. Carbonate samples and standards were reacted on the online common acid bath system with automated sample gas purification described previously  (Passey et al. , 2010). Acid digestion of carbonate minerals was carried out at 90 • C. For full details of analytical methods see previous publications  (Huntington et al. , 2009; Passey et al. , 2010). In brief, 8-10 mg of calcium carbonate samples were crushed and reacted in phosphoric acid on an automated online acid reaction system  (Passey et al. , 2010)  where evolving CO 2 gas is immediately frozen in a liquid nitrogen trap. Sample gases are passed through a Porapak Q 120/80 mesh GC (gas chromatograph) column held at -20 • C to remove potential organic contaminants.
08 ‰ was made. We report data using both the stochastic reference frame for 47 values (as reported in previous studies such as  Ghosh et al. , 2006)  and the "absolute reference frame" of  Dennis et al. (2011) , which assumes a certain value for the difference between heated gases and CO 2 gas standards equilibrated at other temperatures. As the majority of data here was collected before the proposition of the absolute reference frame, we convert 47 values to this reference frame using carbonate standards that were analyzed over the analytical time period. Accepted 47 values for Carrara marble and 102-GC-AZ01 on the absolute reference frame determined in our laboratory are 0. 392 ‰ and 0. 724 ‰ respectively  (Dennis et al. , 2011)  and these were used to construct an empirical transfer function to generate 47 values on the absolute reference frame, as described previously  (Dennis et al.
The relationship between temperature and 47 values in bivalve mollusks An initial study of the temperature effects on 47 values in modern bivalve mollusks examined three samples  (Came et al. , 2007). Here we greatly expand the number of specimens measured as well as the range of temperatures encompassed by the calibration. We present data both relative to the stochastic reference frame (to aid comparison with previously published data) and in the recently proposed absolute reference frame (Tables  1-6  and S1 ). The most direct analysis of our data (i. e. involving a minimum of calculations) is the empirical correlation between known growth temperature and 47 value of bivalve carbonate relative to the stochastic reference frame, using a 90 • C phosphoric acid digestion reaction (Fig. 2 ; Table  3 ).
This is the temperature that is now standardly used on our automated online sample reaction and gas purification systems  (Passey et al. , 2010). We then applied the empirically determined acid digestion correction of 0. 08 ‰ to derive data relative to the stochastic distribution that could be compared to previously published data collected on CO 2 produced by digesting carbonates in phosphoric acid at 25 • C (Fig. 2 ). Linear regressions through each dataset are presented in Fig. 2 , and are tabulated with calculated uncertainties and alongside previously published regressions in Table  4. Individual bivalve samples generally conform reasonably well to the temperature relationship defined by the total population of bivalve data. However a small number of samples, for example the specimen of Zygochlamys patagonica, show a significant departure from this relationship (i.
e. fall outside the 95 % confidence intervals of the linear regression; Fig. 2 ). This appears to represent a unique property of the sample (possibly a "vital effect") on 47 rather than an imprecise measurement as the result is confirmed by analysis of CO 2 extracted from this specimen 6 times (Table  2 ). The Levitus atlas of ocean temperatures also calls for a minor difference in mean annual temperature (∼ 8 • C) versus warm summer month (∼ 9 • C) temperature at the location and water depth on the Patagonian shelf where this sample was recovered from. Therefore if the database is correct, then  1 Cultured specimen growth temperature is accurate to within 0. 5 • C on average (see methods). For field collected specimens temperatures correspond to average temperatures for the three warmest months (assumed to be the predominant growing season), it is assumed that there is a 1 • C error in growth temperatures on average.
Ocean temperatures determined from the Levitus database. All temperatures are rounded to the nearest integer. 2 C, calcite; A, aragonite. refers to a mixed mineralogy with one mineral predominating. For the purpose of isotope calculations the dominant mineralogy is used. 3 Represents the number of distinct extractions of CO 2 from a sample, which are then purified and analyzed. 4 Relative to the stochastic distribution. Also referred to as data in the Caltech intralaboratory reference frame. Includes the acid digestion correction of 0. 08. ±. Values are 1 s. e. 5 Values given on the absolute reference frame. incorrect attribution of the season of growth to the summer months in Fig. 2  does not seem a likely explanation  (Levitus and Boyer, 1994). Additional work on specific taxa will be needed to confirm this observation.
Amongst the most significant departures from previous calibration lines are those from both calcitic and aragonitic specimens forming in the coldest environments, near-freezing shallow marine waters of the Ross Sea off Antarctica that do not reach temperatures significantly above 0 • C all year. The R 2 value of our bivalve mollusk calibration line is 0. 7258 (Table  4 ) using data on the absolute reference frame, and the standard deviation of the residuals (SDR) is 0. 017 ‰. This suggests that there is somewhat larger variability in bivalve 47 data compared to other biogenic calibration datasets. For example the linear regression through the foraminifera calibration of  Tripati et al. (2010)  has an R 2 value of 0. 8998 and a SDR of 0. 014 ‰, and for the study of corals by  Thiagarajan et al.
(2011)  the R 2 value is 0. 8703 with a SDR of 0. 015 ‰  (Tripati et al. , 2010; Thiagarajan et al. , 2011). It is possible that this reflects very subtle biological or mineralogical effects on bivalve 47 data, although, as we describe below, we cannot resolve these effects in our dataset. In the case of field collected bivalves in the figures and regression analysis presented we assumed that preferential growth occurred in the three warmest summer months. However we accept that many taxa do also grow at other times of the year and so in order to assess the impact of our assumption on the resulting regression lines through 47 versus temperature data we also created a regression line using mean annual water temperatures (data not shown) for field collected specimens. The slope of a linear regression line through all bivalve data including field collected specimens assumed to reflect mean annual temperature (rather than warm month average temperatures as in figures and tables) is 0.
Comparison of bivalve 47 calibration with other theoretical and empirical calibrations A linear regression through the plot of 1/T versus 47 values for our measurements from bivalves produces a significantly shallower slope than a regression through previously published calibration materials analyzed in our laboratory (Fig. 3 ). Previous publications did not use the same software or approaches for calculating linear regressions (e. g. ,  Ghosh et al. , 2006; and Huntington et al. , 2009). Therefore in order to compare regressions precisely, as in Figs. 3  and 4 , we recalculate all linear regressions using GraphPad Prism software  (Zar, 1984)  and it is these values that are presented in Table  4. In practice however these different methods do not yield slopes and intercepts that are markedly different; for example the linear regression presented by  Ghosh et al.
(2006)  yielded a slope of 0. 592, whereas using the software utilized here we yield a slope of 0. 598. Linear regressions presented here do not take into account errors in carbonate formation temperatures or isotope measurements; in this dataset these tend to be quite similar on average and do not significantly impact the slope of the regression (data not shown). The slopes of the bivalve calibration regression and the  Ghosh et al. (2006)  inorganic calcite regression are significantly different (Table  5 ). Additionally, the bivalve mollusk calibration is shown to be significantly different than a compilation of published biogenic data from our laboratory (Table 5). The slopes of the bivalve calibration regression and the inorganic calibration regression of  Dennis and Schrag (2010)  are not significantly different (Table  5 ).
However, the intercepts of the Dennis and Schrag regression and our bivalve data are significantly different (p = 0. 0012). Thus, even though the slopes of these calibrations are statistically 1 Cultured specimen growth temperature is accurate to within 0. 5 • C on average (see methods). For field collected specimens temperatures correspond to average temperatures for the three warmest months (assumed to be the predominant growing season), it is assumed that there is a 1 • C error in growth temperatures on average. Ocean temperatures determined from the Levitus database. All temperatures are rounded to the nearest integer. 2 C, calcite; A, aragonite. refers to a mixed mineralogy with one mineral predominating. For the purpose of isotope calculations the dominant mineralogy is used. 3 Represents the number of distinct extractions of CO 2 from all samples, which are then purified and analyzed.
4 Relative to the stochastic distribution. Also referred to as data in the Caltech intralaboratory reference frame. Includes the acid digestion correction of 0. 08. ±. Values are 1 s. e. 5 Values given on the absolute reference frame. indistinguishable, there could be an offset in the absolute values of the two. We also note that the apparently higher variability in the bivalve mollusk dataset compared to other biogenic calibration datasets is taken into account by the statistical analysis of slopes presented in Table  5  and so this variability itself cannot explain the statistically significant differences in slopes we observe. In order to consider whether the slope of the bivalve linear regression could be significantly effected by a few anomalous datapoints we tested the effect of excluding the five specimens recovered from the coldest temperatures from Antarctica (Laternula elliptica and Adamussium colbecki) that are also amongst the most different from the calibration line of  Ghosh et al.
(2006) , yielding 47 values of 0. 72-0. 74 ‰ rela-tive to the stochastic distribution (Table  3 ) compared value of 0. 80 ‰, which is predicted for carbonates growing at -1 • C if they conformed to the calibration of  Ghosh et al. (2006). One possibility is that cold environments favor the expression of kinetic isotope effects on the 13 C-18 O bond abundance in carbonates, as, for example, the rate of reaction for the hydration of CO 2 in solution decreases significantly between 25 and 0 • C and is a potential source of disequilibrium isotope effects in the dissolved inorganic carbon pool from which carbonate forms  (Johnson, 1982; Zeebe, 2009). In order to assess potential bias from these datapoints on regression lines we recalculated the linear regression through our dataset excluding taxa that grow in coldest environments and give potentially anomalous 47 values.
Exclusion of the 1 Cultured specimen growth temperature is accurate to within 0. 5 • C on average (see methods). For field collected specimens temperatures correspond to average temperatures for the three warmest months (assumed to be the predominant growing season), it is assumed that there is a 1 • C error in growth temperatures on average. Ocean temperatures determined from the Levitus database. All temperatures are rounded to the nearest integer. 2 C, calcite; A, aragonite. refers to a mixed mineralogy with one mineral predominating. For the purpose of isotope calculations the dominant mineralogy is used. 3 Represents the number of distinct extractions of CO 2 from a sample, which are then purified and analyzed. 4 Relative to the stochastic distribution. Also referred to as data in the Caltech intralaboratory reference frame.
Calcite versus aragonite Theoretical calculations predict that there would be an offset between 47 values derived from calcite compared to aragonite  (Schauble et al. , 2006; Guo et al. , 2009). However measurements from foraminifera and corals have not resolved any mineralogical effect  (Tripati et al. , 2010; Thiagarajan et al. , 2011). In our mollusk dataset there is a slight offset between the slopes of regression lines between calcitic and aragonitic mollusks (Fig. 4 ), however the offset is in the opposite direction to that predicted from theory  (Schauble et al. , 2006; Guo et al. , 2009). The slopes of linear regressions through the temperature-47 data for calcitic and aragonitic taxa (Fig. 4 ) were not significantly different (p = 0. 520). If a difference between calcitic and aragonitic mollusks exists, then it is not easily resolvable.
In some cases bivalves that precipitate shells with mixed mineralogy were selectively sampled to only acquire the calcite phase, such as the M. edulis specimens grown at Bangor University  (Freitas et al. , 2008). However, in other cases this distinction was not made and both mineralogies were sampled, as detailed in Table  4. For the calcite versus aragonite comparison samples with mixed mineralogy were excluded. When comparing the regression lines through the aragonite data to other calibrations (Table  5 ) it is worth noting that there does not appear to be enough data to statistically determine which of the two different inorganic calcite calibration lines  (Ghosh et al. , 2006; Dennis and Schrag, 2010)  the aragonitic mollusk data fits best with. Therefore it remains possible that the lack of a mineralogical difference in our study could be further resolved in the future with larger datasets.
Discussion The data presented here reaffirms the potential of 47 measurements to provide independent constraints on mineral formation temperatures and provides an empirical calibration that can be applied to paleoclimate studies using bivalve   S1  for the data used for these regression line calculations. b Includes coral data from  Ghosh et al. , 2006 (but excludes Red Sea Porites) , and data from  Ghosh et al. (2007) ,  Came et al. (2007 ), Tripati et al. (2010) ,  Eagle et al. (2010), and Thiagarajan et al. (2011). See Table  S1  for values for these data. c Excluding data from the five individuals of Laternula elliptica and Adamussium colbecki (which are Antarctic specimens from the coldest environments sampled in this study) as a means for determining whether the calibration slope could be significantly influenced by these samples alone.
d Excluding specimens with mixed mineralogy. e Linear regressions through previously published data are all recalculated here using GraphPad Prism software  (Zar, 1984)  so that they are directly comparable to the new mollusk data presented here, and as a result may have slight differences from the slopes and intercepts given in original publications at the third or fourth decimal place. All regressions are on data that include an acid digestion temperature correction where appropriate  (Passey et al. , 2010). Errors are given as 1 s. e. a Linear regression lines through different subsets of our mollusk 47 calibration dataset in the first column are statistically compared to using ANCOVA tests  (Zar, 1984)  to linear regressions through other previously published calibration studies datasets.
Calculations are done with values on the absolute reference frame (ARF). The table displays the ANCOVA p value and whether the two slopes being compared are statistically different; (Y) = Yes, (N) = No. In this case we consider a p value < 0. 05 as indicating statistically significant differences between the two slopes. b Excluding the five specimens of Laternula elliptica and Adamussium colbecki (which are specimens from the coldest Antarctic environments) as a means for determining whether the calibration slope could be significantly influenced by these samples alone. c Excluding specimens with mixed mineralogy. d Includes coral data from  Ghosh et al. (2006)  (but excludes Red Sea Porites), and data from  Ghosh et al. (2007) ,  Came et al. (2007 ), Tripati et al. (2010) ,  Eagle et al. (2010), and Thiagarajan et al.
(2011). See Table  S1  for values for these data. mollusks. We also show that changing solution pH and carbonate chemistry should not be a confounding factor in the interpretation of bivalve based 47 or δ 18 O measurements, at least in the taxa studied, and that there is no significant mineralogical difference between calcite and aragonite. The errors in slope and intercepts for linear regression lines given in Table  4  highlight that successful calibration of the carbonate "clumped isotope" thermometer is dependent on having large datasets. For example, a linear regression through the initial inorganic calcite calibration dataset  (Ghosh et al. , 2006)  has much larger uncertainties than a calibration line based on all the published biogenic calibration data from our laboratory due to having fewer datapoints.
, 2013) , although as these measurements were conducted using a very similar methodology to that used in our study the similarity between our calibration slopes does not entirely resolve the possible methodological differences between calibration studies described below. There are two possible explanations that are immediately apparent for the differences between calibration lines generated from different materials in our laboratory. First, the bivalve mollusk data presented here was obtained using the automated online sample reaction system described in  Passey et al. , 2010 , whereas the in-depth calibration studies of corals, foraminifera and coccoliths were conducted using offline reactions with cryogenic and gas chromatography cleanup steps performed manually  (Passey et al. , 2010; Tripati et al.
The data from foraminifera at cold temperatures is relatively sparse, with some samples from the Arctic Ocean showing deviations from the Caltech inorganic calcite calibration and so are analogous to the mollusk data presented here, but other datapoints from specimens from slightly warmer environments fall closer to the calibration of  Ghosh et al. (2006), and Tripati et al. (2010). This highlights the relative paucity of data from carbonates forming at low temperatures and this is an obvious area to focus on in future calibration studies. Bivalve mollusks frequently precipitate their shells close to equilibrium with respect to oxygen isotopes, with maximum deviations typically in the range of 0. 5 ‰ (e. g. ,  Horibe and Oba, 1972; Romanek and Grossman, 1989; Grossman and Ku, 1986; Barrera et al.
Finally, we note that even though a mineralogical difference between calcite and aragonite could not be resolved in our dataset it is still possible that very subtle mineralogical effects do exist and these effects contribute to the variability in measured 47 values. Larger datasets may be required to constrain this possibility with more certainty. In conclusion, if the experimental effects described above can be either ruled out or better constrained, we will be able to say more about whether there may be small biological fractionations in 47 that differ between corals, foraminifera, and bivalves, and why these fractionations are most apparent at cold temperatures. Supplementary material related to this article is available online at: http://www. biogeosciences. net/10/ 4591/2013/bg-10-4591-2013-supplement.
Ries et al. (2009)a for individual cultured mollusk specimens grown at ambient carbonate saturation state and undersaturated conditions. Culture conditions and seawater chemistry measurements are from Ries et al. (2009). 1 Represents the number of distinct extractions of CO 2 from all samples, which are then purified and analyzed. 2Relativetothestochasticdistributiononly. Also referred to as data in the Caltech intralaboratory reference frame. Includes the acid digestion correction of 0. 08. ±. Values are 1 s. e. 3Values given on the absolute reference frame. Ca 2+ ][CO2- 3] / K sp , where K sp is the stoichiometric solubility product of aragonite. aragonitewas calculated as described inRies et al. (2009).
Acknowledgements. This work was funded by  National Science Foundation  grants  ARC-1215551  to  R. A. Eagle  and  A. K. Tripati ,  EAR-1024929  to  R. A. Eagle  and  J. M. Eiler , and  EAR-0949191  to  A. K. Tripati. A. K. Tripati  is also supported by the  Hellman Fellowship program. We thank  Chris Richardson  ( Bangor University ) for provision of the field collected Arctica islandica specimens. Culture of bivalves in Kiel, Germany, was funded by the  German Science Foundation  ( DFG Ei272/21-1 , to  Anton Eisenhauer ) and the  European Science Foundation (ESF)  Collaborative Research Project  CASIOPEIA  ( 04 ECLIM FP08 ). Determination of bivalve mineralogy by  J. B. Ries  was funded by  National Science Foundation  grant  OCE-1031995. ISMAR-CNR Bologna  scientific contribution n. 1781.
R. A. Eagle et al. : 13 C-18 O bond ordering in bivalve mollusksTable 4. 4 Slopes and intercepts of linear regressions through 47 and temperature data for samples with known growth temperatures. Relative to the stochastic distribution Absolute reference frame Dataset Slope e 1 s. e. Intercept 1 s. e. R 2 Slope e 1 s. e. Intercept 1 s. e. R 2 Inorganic calcite a 0. 0598 0. 0094 -0. 0248 0. 1046 0. 8896 0. 0620 0. 0099 0. 0021 0. 1095 0. 8877 Ghosh et al. (2006) Inorganic calcite a 0. 0316 0. 0036 0. 2697 0. 0382 0. 8587 0. 0340 0. 0038 0. 3155 0. 0408 0. 8600 Dennis and Schrag (2010) Published biogenic 0. 0550 0. 0019 0. 0267 0. 0223 0. 9140 0. 0559 0. 0019 0. 0708 0. 0232 0. 9105 data compilation b All bivalve mollusks 0. 0341 0. 0041 0. 2719 0. 0496 0. 7246 0. 0362 0. 0044 0. 3140 0. 0527 0.
Microarray data analysis A Principal Component Analysis (PCA) was performed suing the GeneANOVA software  [34]  to assess the internal consistency of different transcriptional data sets and to obtain the proportion of variance for each principal component. The intensity of each spot was log 2 -transformed and normalized according to the cyclic Loess technique  [35]  using the Limma package  [36]  and R software. Statistical analysis was made using TMeV6. 1 (Mev_4_7_4) software  [37, 38]. ANOVA were performed with high stringency p value of < 0. 01 with a Bonferroni's correction. Hierarchical clustering and K-Means clustering were performed after ANOVA using TMeV on the statistically significant transcripts with Pearson's correlation as distance matrix and 50 maximum iterations.
Five nanograms of cDNA of each sample were processed under the following PCR conditions: one cycle of 5 min at 95°C followed by 45 cycles consisting of 10 sec at 95°C and 30 sec at 60°C. Primers used are listed in Table  1. The reference gene used in this study was EF1α (Crassostrea gigas EFα1 AB122066). The choice of the reference gene was determined by an expression analysis of the more stable and frequency used housekeeping genes identified by Dheilly et al. [28]. From normalized data of the microarray and also in quantitative PCR data, the coefficient of variation of GAPDH, EFα1, Actin, HKG1, Sec61, HKG3 and ARF1 was measured and was lowest for EFα1. Comparison among the four conditions of infectious level used in this study also showed that EFα1 was most stable. The validation of the selected genes by qPCR was also tested by the calculation of the coefficient of correlation between the data of microarray and qPCR.