Isotope data and associated attributes for 25 thermokarst lakes along the Inuvik – Tuktoyaktuk Highway, 2018
Bibliographic record
Abstract
This dataset contains water isotope concentrations measured from 25 lakes at five time points in 2018, along with snowpack and rainfall isotope concentrations from 2018. The lakes spanned a ~70km stretch of the Inuvik to Tuktoyaktuk Highway (ITH). Rainfall samples were collected in Inuvik, while snowpack samples were collected within the vicinity of the Trail Valley Creek Research Station. The dataset also contains lake and watershed characteristics for the 25 lakes that were sampled for water isotope analysis. Lake-specific properties include surface area, watershed position, depth, latitude, longitude, and elevation, while watershed-specific properties include surface area, average hillslope angle, drainage density, and ice-wedge polygon coverage. Watersheds were delineated using a 2-metre resolution digital elevation model and the D8 algorithm. Within each watershed, the areas of ice-wedge polygons were identified visually from satellite imagery and digitized manually. Drainage density was calculated as the length of all flowpaths with a contributing area greater than 5000 m2, and then divided by the total area of the watershed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.014 |
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".