Field data on sea ice restoration by artificial flooding in subarctic Canada
Bibliographic record
Abstract
This dataset contains a variety of temporal, spatial, and visual measurements describing sea ice restoration by artificial flooding between 18 February and 6 May of 2025 in the Milan Arm of Pistolet Bay in northern Newfoundland, Canada. Data are provided as .CSV, .XLSX, .JPG, .MP4, and .PDF files, with metadata outlined in the README.PDF file. The data include: ice thickness; snow and water depth; air, water, snow, and ice temperature; ice salinity; snow density; snow and ice composition; phytoplankton content in ice and water; aerial drone images, both optical and thermal, and video; timelapse camera videos; flooding information; solar irradiance (downwelling and upwelling); wind speed and direction; and barometric pressure. The dataset can be used to assess the impact of artificial flooding on the accretion and ablation of snow-covered first-year sea ice, and on phytoplankton content in sea ice in subarctic conditions. The dataset can be used to investigate floodwater distribution over and through snow on sea ice. A full description of the data and experimental methods has been published in Data in Brief (see link below). For questions about the data, contact Cody C. Owen (cody@arcticreflections.earth).
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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 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".