Lethality of Sea Lamprey Attacks on Lake Trout in Relation to Location on the Body Surface
Bibliographic record
Abstract
We compared the locations of healed attack marks of the sea lamprey Petromyzon marinus on live lake trout Salvelinus namaycush with those of unhealed attack marks on dead lake trout to determine if the lethality of a sea lamprey attack was related to attack location. Lake trout were collected from Lake Ontario, live fish with gill nets in September 1985 and dead fish with trawls in October 1983−1986. Attack location was characterized by the percent distances from snout to tail and from the ventral to the dorsal midline. Kolmogorov−Smirnov two-sample tests did not detect significant differences in the distribution of attack location along either the anteroposterior axis or the dorsoventral axis. When attack locations were grouped into six anatomical regions historically used to record sea lamprey attacks, dead fish did not exhibit a significantly higher proportion of attacks in the more anterior regions. Even if the differences in attack location on live and dead fish were significant, they were too small to imply substantial spatial differences in attack lethality that should be accounted for when modeling the effects of sea lampreys feeding on lake trout. We suggest that the tendency for sea lamprey attacks to occur on the anterior half of the fish is related to the lower amplitude of lateral body movement there during swimming and thus the lower likelihood of being dislodged.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".