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Record W4303432290 · doi:10.1002/tafs.10389

Lake Superior Kiyi reproductive biology

2022· article· en· W4303432290 on OpenAlexaboutno aff
Mark R. Vinson, Matthew E. Herbert, Amanda S. Ackiss, Jamie A. Dobosenski, Lori M. Evrard, Owen T. Gorman, Joshua F. Lyons, Sydney B. Phillips, Dan L. Yule

Bibliographic record

VenueTransactions of the American Fisheries Society · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersCisco Systems
KeywordsFecundityBiologyReproductive biologyBathymetryFisherySex ratioBycatchZoologyEcologyFish <Actinopterygii>OceanographyPopulationDemographyGeology

Abstract

fetched live from OpenAlex

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?”

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.960
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.010
GPT teacher head0.214
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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