Supplementary material to "Biogeochemical evolution of ponded meltwater in a High Arctic subglacial tunnel"
Bibliographic record
Abstract
Methods S1: Water isotope fractionation modelA model was developed using the principles of isotopic fractionation to estimate the isotopic composition of incremental ice, incremental vapor, and residual water as a hydraulically isolated waterbody progressively freezes and evaporates.Natural waters are composed of hydrogen, which has two stable isotopes ( 1 H and 2 H or deuterium, D) and oxygen, which has three stable isotopes ( 16 O, 17 O and 18 O).H2O molecules in natural water can therefore have one of nine possible molecular weights.Due to differences in the vibrational energies of the bonds of these molecules, the freezing of water under equilibrium conditions results in heavier molecules fractionating more readily into the solid ice, while lighter molecules fractionate more readily into the remaining liquid water.During evaporation, lighter molecules fractionate more readily into the vapor, leaving the residual water relatively enriched in the heavier molecules.Eq S1 to Eq S4 were used to (1) determine the freeze:evaporation ratio that yielded δ 18 O-δ 2 H values for incremental ice and residual water close to δ 18 O-δ 2 H values of the ice and water samples measured in this study, and 2) model the evolution of δ 18 O and δ 2 H of incremental ice, incremental vapor, and residual water as an isolated waterbody progressively freezes and evaporates (at relative rates determined in (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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.513 | 0.121 |
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".