Conservation of Population Diversity of Pacific Salmon in Southeast Alaska
Bibliographic record
Abstract
Abstract We analyzed intraspecific variation in selected biological characteristics of five species of Pacific salmon Oncorhynchus spp. in southeast Alaska and adjacent areas of Canada with a particular interest in describing the variation among populations and suggesting conservation priorities to preserve existing variation. We identified traits that showed high levels of among-population variation, evaluated the interspecific consistency of variation patterns, and noted the relationship of these traits to potential adaptive variation. In addition, we graphically identified populations with distinctive phenotypic and demographic characteristics as outliers from the distribution of mean values of traits taken from populations throughout the region. We also reviewed allozyme surveys to identify populations that differed in terms of the geographic clustering patterns of allele frequencies. Approximately 9,000 salmon populations occur in the study area, and sufficient data were available from 2,062 (23%) of them to analyze at least one characteristic. We identified 47 populations represented by adequate data sets that have distinctive characteristics. An additional 35 populations, represented by limited samples or unusual nominal traits, may be regionally distinctive. Of the 47 adequately sampled, distinctive populations, 22 met our criteria for conservation consideration: (1) high potential for adaptive variation (including distinctive run timing), (2) a distinctive trait combined with high spawner abundance or allozyme frequencies that diverge from geographic clustering patterns, and (3) more than one distinctive characteristic or freshwater habitat shared with other distinctive populations. Freshwater habitats for 6 of those 22 populations are located in watersheds that do not have restrictive land use designations and warrant the highest conservation priority.
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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 | 0.001 | 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 | 0.001 | 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".