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Record W2023038969 · doi:10.1080/15222055.2012.711277

Sperm Quality of Hatchery-Reared Lake Trout Throughout the Spawning Season

2012· article· en· W2023038969 on OpenAlexafffund
Katelynn Johnson, Ian A.E. Butts, Chris C. Wilson, Trevor E. Pitcher

Bibliographic record

VenueNorth American Journal of Aquaculture · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsTrent UniversityMinistry of Natural Resources and ForestryUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WindsorMinistry of Natural Resources
KeywordsHatcheryBiologyTroutSpermHuman fertilizationLongevitySeasonal breederSalvelinusAnimal scienceReproductionSeasonalitySperm motilityEcologyFisheryZoologyFish <Actinopterygii>BotanyAnatomy

Abstract

fetched live from OpenAlex

Abstract The objective of this study was to investigate variation in sperm quality metrics (motility, velocity, linearity, longevity, and density) of hatchery-reared Lake Trout Salvelinus namaycush throughout the spawning season. Seasonal variation in sperm quality was investigated using both a regression and repeated-measures approach. Sperm was collected from the same 16 individuals over four sampling periods, separated by 3-week intervals. Regression analyses showed that 7–27% of the variation in sperm traits could be explained by seasonal variation, indicating that seasonality can have a significant impact on the quality of sperm. Significant positive linear relationships were found for percent motility and linearity at 5 s postactivation. Significant negative quadratic relationships were found for velocity at 5 s postactivation, longevity, and density, whereas a positive quadratic relationship was found for linearity at 10 s postactivation. Repeated measures ANOVAs showed a significant effect of season for percent motility and linearity at 5 and 10 s postactivation, velocity at 10 s postactivation, and longevity. Our findings are important for optimizing fertilization protocols for hatchery production and can also be used to understand reproductive biology and ecology of wild Lake Trout stocks.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.018
GPT teacher head0.284
Teacher spread0.266 · 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 teacher head, 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

Citations17
Published2012
Admission routes2
Has abstractyes

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