Report prepared for: Freshwater Fisheries Society of British Columbia and the Kootenai Tribe of Idaho Contributing authors:
Bibliographic record
Abstract
Seven tetranucleotide microsatellite loci were used to define and assess kokanee (Oncorhynchus nerka) populations and their potential stock structure in the Kootenai River Basin in Idaho and Montana, and in the Kootenay Basin (Koocanusa Reservoir) in Montana and British Columbia. A total of 277 samples were analyzed: 60 from Koocanusa Reservoir tributaries in British Columbia, 30 from Kootenai River tributaries in Idaho, and 187 from Kootenay Lake or Kootenay River tributaries in British Columbia. Samples were primarily collected from kokanee spawning tributaries. DNA was extracted from individual fish fin samples using Qiagen kits and extraction methods. PCR products were genotyped on Applied Biosystems Model 3100 and Model 3730 genetic analyzers. Alleles were scored with Genescan, Genotyper, and Genemapper software from Applied Biosystems. Representative PCR products previously scored on the 3100 were rerun on the 3730 to harmonize data sets between the two genetic analyzers. The number and frequency of alleles per locus and per sample collection, and estimates of heterozygosity were calculated using the Excel Microsatellite Tools program. Allelic richness (average number of alleles per locus corrected for sample size) was determined using the FSTAT program. The Genpop program was used to test for heterozygote deficiency and linkage
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.178 | 0.041 |
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".