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Record W4413784293 · doi:10.3755/galaxea.g27o-11

The Titan Triggerfish (<i>Balistoides viridescens</i> - Bloch and Schneider, 1801): An Ecosystem Engineer in a Feedback Loop

2025· article· en· W4413784293 on OpenAlexfundno aff
William R. Allison

Bibliographic record

VenueGalaxea Journal of Coral Reef Studies · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsTitan (rocket family)PhysicsAstrobiology

Abstract

fetched live from OpenAlex

Coral reef biodiversity relies on a dynamic balance between destruction and renewal. In this dynamic, the titan triggerfish (Balistoides viridescens) plays a paradoxical role—visibly damaging reefs by breaking coral rock, yet enhancing structural integrity by preying on bioeroding bivalves that undermine reef frameworks. This investigation evaluates B. viridescens as an ecological engineer in a feedback loop, examining how its foraging influences reef structure. Titan feeding and the resulting bioerosion were observed in various locations, including Lakshadweep, Indonesia, and many locations in the Maldives. An exploratory study was conducted on Kunfunadhoo Reef in Baa Atoll, Maldives. Surveys quantified coral cover, bivalve density, triggerfish abundance, and bioerosion rates. Coral mortality in 1998 produced abundant substrate for boring bivalve invasion, and by 2004, erosion of dead branching and tabulate corals increased the exposure of coral rock colonized by boring bivalves—a preferred prey of B. viridescens. While titan bioerosion rivaled that of individual scarids, parrotfish collectively eroded far more substrate. Although titan bioerosion may facilitate reef renewal by clearing weakened substrate, its benefits depend on CaCO3 accretion exceeding erosion—a balance increasingly threatened by ocean warming and acidification. This study explores the titan's dual role in reef erosion and construction, and indicates the need for broader spatial and temporal assessments.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.109
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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