The influence of below-cloud secondary effects on the stable isotope composition of hydrogen and oxygen in precipitation at Calgary, Alberta, Canada
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Bibliographic record
Abstract
Stable isotope compositions of hydrogen (δ<sup>2</sup>H) and oxygen (δ<sup>18</sup>O) for short-term precipitation samples (n = 436) collected at Calgary, Alberta, Canada, between January 1997 and December 2001 were determined. Linear regression between δ2H and δ<sup>18</sup>O values of snow and large amount rain samples (≥4 mm) yielded correlation equations δ<sup>2</sup>H = 7.72 × δ<sup>18</sup>O + 5.02 and δ<sup>2</sup>H = 7.50 δ<sup>18</sup>O + 0.27, respectively. In contrast, correlation equations between δ<sup>2</sup>H and δ<sup>18</sup>O values for small amount rain samples (≤4 mm) resulted in progressively lower slope and intercept values with decreasing precipitation. Correlations of isotope data with parameters such as local temperature, relative humidity, and precipitation amount provided evidence that small amount rain samples undergo secondary evaporation accompanied by mass dependent isotope fractionation during their descent from the cloud base to the ground. Hence, the isotope compositions of precipitation at Calgary, and likely also at other locations in the North American Great Plains region, are influenced by below-cloud secondary effects. Since about one-third of the precipitation events in the 5-yr observation period were rain samples accumulating less than 4 mm, below-cloud secondary effects resulted in a slight decrease of slope and intercept values of the local meteoricwater line (δ<sup>2</sup>H=7.43 × δ<sup>18</sup>O-2.79) calculated using amount-weighted monthly average δ<sup>2</sup>H and δ<sup>18</sup>O values compared to equations based on isotope data for snow and large amount rain events only. The correlation equation (δ2H = 7.11 δ<sup>18</sup>O-11.60) calculated using δ<sup>2</sup>H and δ<sup>18</sup>O values of individual samples (non-amount weighted) yielded the lowest slope and intercept values caused by the significant influence of small amount rain samples.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it