Galápagos sperm whales (<i>Physeter macrocephalus</i>): waxing and waning over three decades
Bibliographic record
Abstract
While population sizes and structures naturally fluctuate over time, rapid within-generation changes are usually driven by shifts in habitat quality and (or) abrupt mortality. We evaluate how sperm whales (Physeter macrocephalus L., 1758 = Physeter catodon L., 1758) responded to the dynamic habit off the Galápagos Islands over 30 years, relating it to variation in prey availability and whaling operations in the tropical Pacific. In the 1980s, males and females were commonly sighted foraging and socializing in the northwest of the archipelago. Sightings decreased during the 1990s; by the 2000s, they became very rare: occasional single foraging males were sighted and females abandoned the archipelago. In the 2010s, whales return to the southern waters, in large groups with apparently more breeding males and calves. The waxing and waning of Galápagos sperm whales are likely caused by environmental shifts together with ripple effects of whaling. Their patchy prey are influenced by variation in sea temperature and productivity, which drives movements of whales in and out of the archipelago. Whaling may have aggravated these movements by leaving an attractive surplus of prey in coastal waters depleted of whales. These findings highlight the magnitude of spatiotemporal scales used by sperm whales and the consequent challenges of assessing population dynamics of long-lived, mobile pelagic species.
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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 | 0.000 |
| 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 | 0.001 | 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".