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Record W2995110229 · doi:10.1126/science.aaz1301

Response to Comment on “Demographic dynamics of the smallest marine vertebrates fuel coral reef ecosystem functioning”

2019· letter· en· W2995110229 on OpenAlexaff
Simon J. Brandl, Renato A. Morais, Jordan M. Casey, Valériano Parravicini, Luke Tornabene, Christopher H. R. Goatley, Isabelle M. Côté, Carole C. Baldwin, Nina M. D. Schiettekatte, David R. Bellwood

Bibliographic record

VenueScience · 2019
Typeletter
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsSimon Fraser University
FundersAgence Nationale de la Recherche
KeywordsCline (biology)Coral reefReefBiomass (ecology)EcosystemMarine ecosystemCoralPopulationEcologyBiologyDisturbance (geology)FisheryCoral reef fishEnvironmental scienceOceanographyGeologyDemography

Abstract

fetched live from OpenAlex

Allgeier and Cline suggest that our model overestimates the contributions of cryptobenthic fishes to coral reef functioning. However, their 20-year model ignores the basic biological limits of population growth. If incorporated, cryptobenthic contributions to consumed fish biomass remain high (20 to 70%). Disturbance cycles and uncertainties surrounding the fate of large fishes on decadal scales further demonstrate the important role of cryptobenthic fishes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.040
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0400.036
Insufficient payload (model declined to judge)0.0110.013

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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations9
Published2019
Admission routes1
Has abstractyes

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