Lake Superior Kiyi reproductive biology
Bibliographic record
Abstract
Abstract Objective The Lake Superior Kiyi Coregonus kiyi is an understudied species being considered for reintroduction into Laurentian Great Lakes where it no longer occurs. Herein, we provide descriptions of Kiyi reproductive biology with the intention of guiding potential gamete collections for propagation. Methods Data were collected on Kiyi spawning timing, spawning locations, spawning season catch rates, length at sexual maturity, sex ratios, fecundity, egg size, and larval occurrences in Lake Superior from 1996–2021. These data were compared to observations made a century prior in Lakes Michigan, Ontario, and Superior. Result Contemporary Kiyi spawning occurred between late December and late January when surface water temperatures cooled to <4°C. Spawning Kiyi were caught almost exclusively in 38.1-mm stretch mesh, as compared to larger meshes (50.8–76.2 mm). Capture depths for developing, ripe, running, and spent female Kiyi were similar and ranged from 82 to 221 m. Fifty percent of female and male Kiyi were classified as sexually mature at ~150 mm total length. Fecundity estimates ranged from 1,578 to 6,720 eggs/female. Mean diameter of unfertilized eggs was 1.7 mm. Recently hatched larval Kiyi were collected at the surface during May–July at 62 of the 113 locations sampled throughout the lake in 2019. Conclusion Our work suggests that Kiyi gamete collection efforts from mid-December through January using 38.1-mm gill-net panels set at bathymetric depths of at least 100 m would maximize the collection of spawning Kiyi and reduce the bycatch of other Coregonus species. Future research questions include the following: (1) “Do Kiyi form spawning aggregations at specific spawning areas, or do they spawn indiscriminately across the lake?”; (2) “Do Kiyi spawn near the bottom or up in the water column?”; (3) “What is the relationship between fall lake overturn and Kiyi spawn timing?”; and (4) “Could summer larval and age-0 Kiyi collections provide an opportunity for establishing a captive broodstock?”
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".