MétaCan
Menu
Back to cohort
Record W1972491229 · doi:10.1890/es13-00259.1

Intermittent breeding in the absence of a large cost of reproduction: evidence for a non‐migratory, iteroparous salmonid

2013· article· en· W1972491229 on OpenAlexafffund
Yolanda E. Morbey, Brian J. Shuter

Bibliographic record

VenueEcosphere · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of TorontoMinistry of Natural Resources and ForestryWestern University
FundersMitacsMinistry of Natural Resources
KeywordsSemelparity and iteroparityBiologyReproductionSalvelinusEcologyTroutPopulationFisheryFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

In long‐lived organisms, intermittent breeding likely evolves as a resource allocation strategy for coping with environmental uncertainty or individual heterogeneity in condition. In fishes, the phenomenon of intermittent breeding is referred to as skipped spawning, and appears to be more common at high latitudes or in migratory species with high accessory costs of reproduction. We used long‐term monitoring data on lake trout ( Salvelinus namaycush ) to test whether key predictions about the frequency of skipped spawning hold in a mid‐latitude population of a species lacking any obvious costs of reproduction beyond the production and fertilization of gametes. We first developed a threshold‐based method to classify skipped spawners based on gonad size, fish size, and fish age. Consistent with life history theory, age‐specific frequencies of skipped spawning were higher in females than males. The frequency of skipped spawning varied among years and was higher in 1994–2011 than in 1938–1959, perhaps because of food web changes over the past century. In temperate lakes, food web structure may be sufficiently variable to favor intermittent breeding in long‐lived iteroparous fishes, despite low accessory costs of reproduction.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.271
Teacher spread0.245 · 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.

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

Citations21
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueEcosphereSame topicFish Ecology and Management StudiesFrench-language works237,207