Genetic variation and evidence for population structure in eastern North Pacific false killer whales (<i>Pseudorca crassidens</i>)
Bibliographic record
Abstract
False killer whales ( Pseudorca crassidens (Owen, 1846)) are incidentally taken in the North Pacific pelagic long-line fishery, but little is known about their population structure to assess the impact of these takes. Using mitochondrial DNA (mtDNA) control region sequence data, we quantified genetic variation for the species and tested for genetic differentiation among geographic strata. Our data set of 124 samples included 115 skin-biopsy samples collected from false killer whales inhabiting the eastern North Pacific Ocean (ENP), and nine samples collected from animals sampled at sea or on the beach in the western North Pacific, Indian, and Atlantic oceans. Twenty-four (24) haplotypes were identified, and nucleotide diversity was low (π = 0.37%) but comparable with that of closely related species. Phylogeographic concordance in the distribution of haplotypes was revealed and a demographically isolated population of false killer whales associated with the main Hawaiian islands was identified (ΦST= 0.47, p < 0.0001). This result supports recognition of the existing management unit, which has geo-political boundaries corresponding to the USA’s exclusive economic zone (EEZ) of Hawai‘i. However, a small number of animals sampled within the EEZ but away from the near-shore island area, which is defined as <25 nautical miles (1 nautical mile = 1.852 km) from shore, had haplotypes that were the same or closely related to those found elsewhere in the ENP, which suggests that there may be a second management unit within the Hawaiian EEZ. Biologically meaningful boundaries for the population(s) cannot be identified until we better understand the distribution and ecology of false killer whales.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".