On the origin of airmasses and their influence on the isotopic composition of precipitation in Canada's western Arctic
Bibliographic record
Abstract
Stable-isotope ratios in ice-wedge ice have been used to reconstruct winter conditions in Siberia and eastern Beringia.Such reconstructions assume that temperature is the principal influence on the isotopic composition of precipitation and, hence, wedge ice.Archived data for over 100 precipitation samples, collected between August 2015 and August 2018 at Inuvik, NT, indicate two distinct populations of δ 18 O values for summer and winter.The regression equation for δ 18 O (‰) on mean temperature (T, °C) for the day of precipitation is δ 18 O = 0.3T -19.3 (R 2 = 0.59, p < 0.01).For summer, the equation is δ 18 O = 0.19T -18.2 (R 2 = 0.01, p = 0.007), and for winter δ 18 O = 0.16T -22.2 (R 2 = 0.15, p = 0.02).The difference between the seasons dominates the regression when all data are pooled, but seasonal data indicate low to no relation between the variables.NOAA's HYSPLIT model was used to trace storms back to where the synoptic system formed as well as the trajectory taken to reach Inuvik.Systems that travel over mountains to reach Inuvik experience considerable fractionation whereas systems from the proximal Beaufort Sea do not.The δ 18 O values for systems originating from the north, south, and west were statistically indistinguishable in both seasons, but southerly systems arrived with the warmest conditions in winter.Separate bulk samples of monthly precipitation collected from 1985-1995 provided a stronger relation between δ 18 O values and monthly mean temperature, but R 2 (0.28) was still low. 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".