Effect of underwater seismic surveys on molting male Long-tailed Ducks in the Beaufort Sea, Alaska
Bibliographic record
Abstract
Large numbers of Long-tailed Ducks (Clangula hyemalis) (10 000 – 30 000) undergo a postnuptial wing molt along barrier islands of the Beaufort Sea, Alaska. To investigate the potential effects of underwater seismic activities on this species, we monitored the number and diving behavior of molting Long-tailed Ducks before, during, and after seismic activities in a seismic area and two control areas nearby between July and September 2001. Aerial surveys documented a decline in duck numbers in both seismic and control areas during the period of seismic activity. We used automated data-collection computers to monitor the presence and diving behavior of radio-equipped Long-tailed Ducks residing within 2.5 km of a series of computer setups located along the barrier islands and on the mainland. A statistical analysis based on a modified before–after control–impact approach found no difference in indices of site fidelity or diving intensity between the seismic area and two control areas. Thus, we found no effect of seismic activity on movements and diving behavior of molting Long-tailed Ducks. These results should be evaluated carefully, however, as logistical and ecological factors limited our ability to detect more subtle disturbance effects. We recommend additional studies on other bird species to fully understand the effects of underwater seismic testing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".