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Climate and body size influence nest survival in a fish with parental care

2007· article· en· W2075739547 on OpenAlexafffund
Cory D. Suski, Mark S. Ridgway

Bibliographic record

VenueJournal of Animal Ecology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsTrent UniversityMinistry of Natural Resources and Forestry
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationMinistry of Natural Resources
KeywordsNest (protein structural motif)EcologyCovariatePhenologyBiologyGeographyDemographyEnvironmental scienceStatistics

Abstract

fetched live from OpenAlex

1. The current study examined the effect of broad-scale climate and individual-specific covariates on nest survival in smallmouth bass over a 20-year period. 2. Large-scale climate indices [winter North Atlantic Oscillation (NAO) and winter El Niño/Southern Oscillation (ENSO)] and body size of parental males were important covariates in nest survival along with nest age and a quadratic trend in survival. 3. We did not find an effect due to a habitat covariate (total effective fetch) or a phenology covariate (degree-days at start of nesting) on nest survival. 4. Male size in the second half of the nesting season was a more influential covariate on nest success than male size in the first half or throughout the nesting period. 5. We present evidence showing that winter NAO/ENSO indices establish limnological conditions the following spring that influence thermal stability of the lake during the nesting period. 6. The combined climate and body size covariates point to nest survival as a function of lagged climate-scale influences on limnology and the individual-scale influence of bioenergetics on the duration of parental care and nest success.

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)0.001
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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

Citations63
Published2007
Admission routes2
Has abstractyes

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