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Record W3105476587 · doi:10.1002/ecs2.3291

Size‐spectra analysis in the estuary: assessing fish nursery function across a habitat mosaic

2020· article· en· W3105476587 on OpenAlexafffund
Karl M. Seitz, William I. Atlas, Benjamin Millard‐Martin, Jared J. Reid, Julian Heavyside, Brian P. V. Hunt, Jonathan W. Moore

Bibliographic record

VenueEcosphere · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans CanadaTula FoundationSimon Fraser UniversityUniversity of British ColumbiaBell (Canada)
FundersHakai InstituteLiber Ero FoundationPacific Salmon FoundationMitacsTula FoundationMcLean Foundation
KeywordsEstuaryHabitatPredationEcologyEcotoneAbundance (ecology)ForagingProductivityEnvironmental scienceFisheryBiology

Abstract

fetched live from OpenAlex

Abstract Estuaries act as nurseries for a wide variety of fish species, potentially providing vital foraging opportunities and refuge from predation for their juvenile residents. Yet, these dynamic environments are comprised of a mosaic of habitat types that span gradients of both salinity and physical habitat structure. Here, we present a novel use of size‐spectra analysis to infer nursery habitat function across the estuary habitat mosaic. Interpreting slope and intercept values of abundance against body mass size‐spectra regressions as indicators of predation risk and production, we constructed spectra for six distinct habitat types across the entire tidal influence of an unindustrialized estuary in coastal British Columbia. Based on catches of >200,000 individual fish representing 30 different species from April through September, the estuary rockweed mudflat habitat had the lowest size‐spectra slope and highest intercept, consistent with lower predation risk and higher production. Size‐spectra coefficients varied seasonally across the ecotone, indicating spatio‐temporal variation in key nursery functions. Size‐spectra can provide insight into key ecological processes of productivity and predation risk across dynamic aquatic habitats.

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 score0.246
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0250.000

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 head0.016
GPT teacher head0.267
Teacher spread0.251 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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