Using Photo ID to Examine Injuries in Eastern Pacific Gray Whales: From Calving to Feeding Grounds and Along the Migratory Corridor
Bibliographic record
Abstract
The Eastern Pacific population of gray whales (Eschrichtius robustus) migrates along the west coast of North America every year; this migration brings them into close contact with shipping lanes and fishing operations which present major anthropogenic (human caused) threats to gray whales. The purpose of this study is to use photo ID of the whales from their feeding and calving grounds, and their migratory corridor to study which body regions are susceptible to both natural and anthropogenic injury and examine the most common types of injury tot he whales. In order to do this, photos were collected from each location and analyzed. Study sites include Bahia Magdalena, BCS, Mexico, Flores Island, BC, Canada, and Redondo Beach and San Pedro, CA United States. Photographs were entered into catalogs for photo ID, and then analyzed to determine the body regions and injuries observed. Types of injuries included: scar, wound, rake mark (from attack by killer whale), entanglement, and fluke (injury on tail that does not fall into another category). It was found that scars, rake marks, and entanglements represented the most common types of injuries, each occurring in about 10% of the whales. Rake marks were found more often on the flukes of the whale than the body, but there was no significant difference in locations of wounds or scars when compared between the body and flukes. From examination of the results of other studies, I estimate that 3-6% of gray whales die from ship strikes. However, because the population is quite large (17,000-22,000 individuals) I conclude that anthropogenic injuries are not representing a significant source of mortality to the Easter Pacific stock of gray whales. I suspect that anthropogenic injuries are more of a threat to smaller populations of cetaceans such as the Western Pacific gray whales and North Atlantic right whale.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".