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Record W2279093128 · doi:10.1093/plankt/fbv080

Variability of mortality rates for<i>Calanus finmarchicus</i>early life stages in the Labrador Sea and the significance of egg viability

2015· article· en· W2279093128 on OpenAlexaffabout
Erica Head, Wendy C. Gentleman, Marc Ringuette

Bibliographic record

VenueJournal of Plankton Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsDalhousie UniversityBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsCopepodBiologyHatchingCalanus finmarchicusAbundance (ecology)CannibalismMortality rateEcologyZoologyCrustaceanCalanusFisheryDemographyLarva

Abstract

fetched live from OpenAlex

Mortality rates of eggs and nauplii are essential for understanding and modelling dynamics of copepod populations. Abundances of Calanus finmarchicus females, eggs and nauplii were determined at 88 stations in the Labrador Sea. Egg production rates (EPRs) and egg and naupliar stage durations were calculated using published relationships with in situ chlorophyll concentration and temperature. The data were used to estimate mortality rates for eggs (ME), eggs and early naupliar stages (ME-NIII) and naupliar stages (MNI–NVI). Estimated mortality rates in the central basin were higher than those on the shelves and within regions generally ME > ME-NIII > MNI–NVI. The "Basic Method" for eggs, arguably the most reliable method, gave a high proportion of seemingly erroneous (negative) values. These became positive when a modified estimation formula was used, which assumes that some eggs being laid could not hatch. Egg hatching success is often <100%, but this is rarely considered when calculating mortality rates, although it affects all estimates that include egg abundances and/or EPRs as variables, mostly at low mortality rates. Egg and early life stage mortality rates were correlated with female abundance, but cannibalism may not be the appropriate interpretation. The issues of egg viability and cannibalism require more careful consideration.

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)0.030
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0300.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.068
GPT teacher head0.360
Teacher spread0.293 · 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

Citations7
Published2015
Admission routes2
Has abstractyes

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