NOVEL APPLICATIONS OF RANDOM FOREST FOR EXPLORING POPULATION STRUCTURE OF ATLANTIC SALMON (SALMO SALAR) IN LABRADOR, CANADA
Bibliographic record
Abstract
The detection of population-genetic structure is useful for understanding patterns of gene flow, population distribution, and wildlife management and conservation. In this work, we examine approaches for inferring the modern genetic structure of Atlantic salmon (Salmo salar). We explore the utility of machine-learning algorithms (random forest, regularized random forest, and guided regularized random forest) compared with FST-ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment within a marine embayment, Lake Melville, Labrador. Using an unpublished SNP dataset for Atlantic salmon and validating our approaches with a published SNP data set for Alaskan Chinook salmon (Oncorhynchus tshawytscha), we demonstrate improved self-assignment accuracy and provide evidence of population structure consistent with F-statistics. We compare the level of population structure in greater Labrador that is resolved using a preliminary panel of SNPs selected with guided regularized random forest with an established panel of 101 microsatellites. We ask if salmon originating from rivers draining into Lake Melville show evidence of discrete genetic population structure relative to those outside of the embayment. Finally, we investigate environmental parameters associated with the observed genetic structure and seek to explain the mechanisms driving genetic differentiation in the area. We highlight the potential for applications of machine-learning approaches in population genetics and uncover fine-scale structure with potential impact on fisheries management techniques.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".