Spatial components of bowhead whale (<i>Balaena mysticetus</i>) distribution in the Alaskan Beaufort Sea
Bibliographic record
Abstract
Bowhead whales (Balaena mysticetus) on their fall migration are exposed to oil exploration activities in the Alaskan Beaufort Sea. While previous research into the effect of industrial noise on whale behavior and distribution has noted significant responses, this research has often proceeded from a parametric statistical framework. To account for the presence of spatially autocorrelated and intercorrelated data, we propose a suite of spatial analysis techniques to assess the distribution of bowhead whales relative to oil exploration activities. Using random resampling techniques and Mantel tests, we analyzed the distribution of bowhead whales around active drilling rigs in 1993. Results from the resampling tests indicated that whales were distributed farther from the drilling rig than they would be under a random scenario. Results from the Mantel tests indicated that in 1993, the spatial pattern of bowhead whale distribution was highly correlated with distance from the drilling rig, indicating that the presence of the drilling rig resulted in a significant temporary loss in available habitat. These techniques offer a new perspective on spatial analysis in the marine realm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".