Lean lake trout are found in spawning condition during spring-summer in lakes Michigan and Huron
Bibliographic record
Abstract
Here we report the first observations of the capture of lean lake trout ( Salvelinus namaycush ) in spawning condition in lakes Michigan and Huron during April–July, which is well outside their normal fall spawning season of September–December. Examination of 5731 lake trout landed by anglers at 56 ports in 2022 and 2023 revealed nine female lake trout possessing body cavities filled with mature, loose eggs and two males in ripe condition. Six fish were hatchery-reared and five were of wild origin. Ages of these fish ranged from 6 to 19 years and, of those where strain could be determined, were members of the Seneca Lake and Lewis Lake genetic strains. Loose eggs were similar in appearance to those found in mature fish in fall on spawning grounds. Histological examination of eggs from four females confirmed all were in some stage of ovarian maturity. Two females had ovulated just prior to capture, and the remaining two ovulated much earlier than the capture date. The adaptive advantage of the alternative seasonal spawning is speculative but may include reduced competition with fall spawners, decreased predation risk for juveniles during winter, and access to greater environmental resources in the early spring and summer.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".