Bibliographic record
Abstract
Archival version of my 2010 Master's thesis Human food production activities can dominate natural systems, altering ecological and evolutionary aspects of the environment. Disease-mediated interactions are of particular concern. For example, parasites may "spill-over" from farms to wildlife. Parasites isolated on farms can evolve resistance to treatment chemicals , but "spill-back" from wildlife to farms may alter evolutionary dynamics. Here, we consider exchange of parasites (Lepeophtheirus salmonis) between wild (Oncorhynchus gorbuscha) and farmed salmon. We derive and analyze discrete-time models that implicitly include wild salmon migrations. First, we extend a standard fisheries model to show parasite exchange affects "line-dominance" in the population ecology of salmon. Second, we extend a classic population genetics model to show that wild salmon can theoretically provide an "ecosystem service" by delaying the onset of chemical resistance in parasites on farms. This service, however is affected by a nonlinear feedback if farm parasites spill-back to affect wild salmon.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".