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Record W2150395353 · doi:10.1086/491720

Rapid Senescence in Pacific Salmon

2005· article· en· W2150395353 on OpenAlexaff
Yolanda E. Morbey, Chad E. Brassil, Andrew P. Hendry

Bibliographic record

VenueThe American Naturalist · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMcGill UniversityToronto ZooUniversity of Toronto
Fundersnot available
KeywordsSenescenceBiologyFisheryEcologyGenetics

Abstract

fetched live from OpenAlex

Any useful evolutionary theory of senescence must be able to explain variation within and among natural populations and species. This requires a careful characterization of age-specific mortality rates in nature as well as the intrinsic and extrinsic factors that influence these rates. We perform this task for two populations of semelparous Pacific salmon. During the breeding season, estimated daily mortality rates increased from 0 to 0.2-0.5 (depending on the year) over the course of several weeks. Early-arriving individuals had a later onset and/or a lower rate of senescence in each breeding season, consistent with adaptive expectations based on temporal variation in selection. Interannual variation in senescence was large, in part because of extrinsic factors (e.g., water temperature). Predation rates were higher in Pick Creek sockeye salmon (anadromous Oncorhynchus nerka) than in Meadow Creek kokanee (nonanadromous O. nerka), but in contrast to evolutionary theory, senescence was not more rapid in the former. Interannual variation may have obscured interpopulation divergence in senescence. Pacific salmon are a promising system for further studies on the physiological, evolutionary, and genetic bases of senescence. In particular, we encourage further research to disentangle the relative importance of adaptive and nonadaptive variation in senescence.

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 score0.335
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations52
Published2005
Admission routes1
Has abstractyes

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