Habitat Selection Scenarios for Molting Waterfowl in the Goose Molting Area of the Teshekpuk Lake Special Area, for NPR-A Integrated Activity Plan/Environmental Impact Statement, 2020
Bibliographic record
Abstract
The dataset consists of a polygon shapefile. Each polygon represents a set of molt units (interconnected lakes used as habitat by molting waterfowl) within the Goose Molting Area of the Teshekpuk Lake Special Area in northern Alaska, in addition to a half-mile or 1-mile wide buffer, that were selected for restrictions on new surface occupancy or infrastructure development by the oil and gas industry. Polygons represent the minimum area necessary to achieve a specific level of population coverage for Pacific Black Brant or Canada Geese that use the Teshekpuk Lake Special Area as molting habitat. All polygons include a 1-mile wide inland buffer along the Beaufort Sea coastline and a 3-mile wide buffer along the shore of Teshekpuk Lake, two areas already identified for surface occupancy/development restrictions by the Bureau of Land Management. Polygons were generated using two buffer widths to reflect uncertainty about the minimum horizontal distance needed to mitigate disturbance effects of industrial activity on molting geese. The boundaries of the Goose Molting Area are defined in the 2013 Record of Decision for the NPR-A Integrated Activity Plan (Bureau of Land Management).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".