Seismic surveys and gray whales near Sakhalin: Multivariate analyses of monitoring data from 2010 Astokh geophysical survey
Bibliographic record
Abstract
Monitoring of a 2010 seismic survey near the feeding grounds of gray whales (Eschrichtius robustus) off Sakhalin Island, Russia, yielded underwater sound recordings and visual observations of nearshore distribution and behavior during the whales' early summer feeding season. These data along with temporal, spatial and environmental non-acoustic impact variables were used in multivariate analyses (MVAs) to investigate effects of sound on i) whale distance from shore, ii) occupancy/abundance and iii) behavioral responses. Sound metrics were estimated through modelling on a grid of density surface cells and on individual whale paths. Acoustic recordings provided refinement factors for modelling results. Distance from shore analyses found no evidence that seismic pulses resulted in a distributional shift. Density MVAs found no significant changes in whale occupancy associated with seismic sound exposure, but found a weak association between decreased densities and a rise-fall pattern of sound exposure over the previous 3 days. These MVAs were limited by sample sizes, and shifts could also reflect changes in prey distribution (not monitored). Behavioral analyses found no significant association between response variables and noise covariates including sound from vessels; however, power analyses on behavioral data found sample sizes to be insufficient to detect small to moderate changes.
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 | 0.001 |
| 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".