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Record W2923659098 · doi:10.1139/cjfas-2018-0264

Are fish associations with corals and sponges more than an affinity to structure? Evidence across two widely divergent ecosystems

2019· article· en· W2923659098 on OpenAlexvenueno aff
Christopher N. Rooper, Pamela Goddard, Rachel E. Wilborn

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFisherySebastesBiologyEcologyGadusHabitatEcosystemGeographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The role of deep-sea coral and sponge ecosystems as habitat for marine fishes has been widely studied, with many finding significant associations, especially for rockfishes. However, rockfishes also thrive in areas largely devoid of corals and sponges. We compared the use of deep-sea corals and sponges by fish species in two ecosystems. Rockfishes (Sebastes spp.) and Pacific cod (Gadus macrocephalus) densities were significantly correlated to structured seafloors at the scale of transects across both regions. Regional effects were not significant for most rockfish species and Pacific cod. At smaller scales only Pacific cod and rockfishes had significant associations with structure. The size of the individual fish and the size of the structure both had significant impacts on the distance of fish from structure, with smaller fish closer to structure and closer to smaller structure. Over half of the individual fishes surveyed were associated with sponges. The implication of this research is that the presence of structure increases the density of rockfishes, and removal of deep-sea corals and sponges is likely to reduce the overall density of rockfishes.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.258
Teacher spread0.224 · 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 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

Citations9
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicCoral and Marine Ecosystems StudiesFrench-language works237,207