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Record W2945449790 · doi:10.1126/science.aav3384

Demographic dynamics of the smallest marine vertebrates fuel coral reef ecosystem functioning

2019· article· en· W2945449790 on OpenAlexafffund
Simon J. Brandl, Luke Tornabene, Christopher H. R. Goatley, Jordan M. Casey, Renato A. Morais, Isabelle M. Côté, Carole C. Baldwin, Valériano Parravicini, Nina M. D. Schiettekatte, David R. Bellwood

Bibliographic record

VenueScience · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsSimon Fraser University
FundersSmithsonian InstitutionAustralian Research CouncilFondation BNP ParibasNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaAgence Nationale de la Recherche
KeywordsCoral reefReefCoral reef fishBiodiversityFisheryEcosystemFish <Actinopterygii>Marine ecosystemMarine protected areaEcologyEnvironmental issues with coral reefsGeographyCoralCoral reef protectionBiology

Abstract

fetched live from OpenAlex

How coral reefs survive as oases of life in low-productivity oceans has puzzled scientists for centuries. The answer may lie in internal nutrient cycling and/or input from the pelagic zone. Integrating meta-analysis, field data, and population modeling, we show that the ocean's smallest vertebrates, cryptobenthic reef fishes, promote internal reef fish biomass production through extensive larval supply from the pelagic environment. Specifically, cryptobenthics account for two-thirds of reef fish larvae in the near-reef pelagic zone despite limited adult reproductive outputs. This overwhelming abundance of cryptobenthic larvae fuels reef trophodynamics via rapid growth and extreme mortality, producing almost 60% of consumed reef fish biomass. Although cryptobenthics are often overlooked, their distinctive demographic dynamics may make them a cornerstone of ecosystem functioning on modern coral reefs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.663

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 science0.0010.001
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 head0.005
GPT teacher head0.176
Teacher spread0.171 · 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.

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

Citations226
Published2019
Admission routes2
Has abstractyes

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