MétaCan
Menu
Back to cohort
Record W2008517612 · doi:10.3354/meps330213

Temperature-dependent temporal variation in the size and growth of Thames estuary smelt Osmerus eperlanus

2007· article· en· W2008517612 on OpenAlexaff
Michael Power, Martin J. Attrill

Bibliographic record

VenueMarine Ecology Progress Series · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSmeltEstuaryContext (archaeology)BiologyEcologyFisheryGeographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Long-term monitoring data collected in the Thames estuary, UK, were used to investigate inter-annual trends in year-end size and growth rate of smelt Osmerus eperlanus (L.). Patterns were related to variations in the thermal opportunity for growth, defined in terms of temperaturedependent cumulative degree-days at estuarine temperatures 5C. Year-end size was positively correlated to the thermal opportunity for growth, whereas growth rate was negatively correlated. Cohort-specific comparisons of estuarine growth patterns indicated significant differences in starting sizes, possibly related to the influences of temperature on early larval development. Interannual differences in within-estuary growth rate as a function of cumulative degree-days were found and suggested thermally dependent growth. Smelt in normal and warm years showed similar rates of growth that differed significantly from rates observed in cold years. Reductions in the proportions of older fish in warm years were also found and interpreted in the context of spatial segregation patterns found in freshwater environments. Overall results suggest that use of mid-estuarine environments during larval development and migration to colder outer-estuarine areas are growth/maturation optimization strategies that enhance individual fitness. Thus, smelt migration phenologies in the Thames estuary appear to be driven by short-term variations in the suitability of thermal habitat.

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 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 score0.016
Threshold uncertainty score1.000

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 science0.0000.001
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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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

Citations16
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueMarine Ecology Progress SeriesSame topicMarine and fisheries researchFrench-language works237,207