Spatial and temporal variation in marking rates and severity of sea lamprey attacks on salmonines in Lakes Michigan and Huron
Bibliographic record
Abstract
The United States and Canada have invested substantial effort to control sea lamprey Petromyzon marinus in the Laurentian Great Lakes and to estimate their effects on lake trout Salvelinus namaycush, a native salmonine undergoing rehabilitation. However, sea lamprey also attack Pacific salmon Oncorhynchus spp. and brown trout Salmo trutta, which contribute to a fishery worth nearly $7 billion USD annually. Marks on surviving hosts are used to assess damages caused by sea lamprey and success of control efforts. We examined spatial and temporal variation in marking rates, mark type, and stage of healing on lake trout, Chinook salmon O. tshawytscha, rainbow trout O. mykiss, coho salmon O. kisutch, and brown trout in Lakes Michigan and Huron. Mean marking rates were highest for lake trout, followed by brown trout, Chinook salmon, rainbow trout, and coho salmon in Lake Michigan, but were several times higher for Chinook salmon than for lake trout (all sizes) and small (533–635 mm) and medium (636–737 mm) rainbow trout in Lake Huron, particularly in summer. Chinook salmon had a lower proportion of healed marks relative to fresh marks compared to lake trout in both lakes, which may indicate differences in post-attack survival. Although lake trout may be the preferred sea lamprey host, Chinook salmon and other species are also suitable and available in Lake Michigan; and Chinook salmon may be a preferred host in Lake Huron. Accounting for alternate hosts could inform fisheries management and improve damage assessments of the sea lamprey control program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".