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Record W4412685744 · doi:10.5194/egusphere-2025-3385

A first predictive mechanistic model of cold-water coral biomass and respiration based on physiology, hydrodynamics, and organic matter transport

2025· preprint· en· W4412685744 on OpenAlexafffund
Evert de Froe, Christian Mohn, Karline Soetaert, Anna‐Selma van der Kaaden, Gert‐Jan Reichart, Laurence H. De Clippele, Sandra R. Maier, Dick van Oevelen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsFisheries and Oceans CanadaMemorial University of Newfoundland
FundersExacte en NatuurwetenschappenHorizon 2020ArcticNet
KeywordsBiomass (ecology)CoralRespirationEnvironmental scienceOrganic matterEcologyBiologyBotany

Abstract

fetched live from OpenAlex

Abstract. Cold-water corals form complex three-dimensional structures on the seafloor, providing habitat for numerous species and act as a carbon cycling hotspot in the deep-sea. The distribution of those important ecosystems is often predicted by statistical habitat suitability models, using variables such as terrain characteristics, temperature, salinity, and surface productivity. While useful, these models do not provide a mechanistic understanding of the processes that facilitate cold-water coral occurrence, and how this may change in the future. Here, we present the results of a mechanistic process-based model in which coral biomass and respiration are predicted from a 3D coupled transport-reaction-model for south-east Rockall Bank (NE Atlantic Ocean). Hydrodynamic forcing is provided by a high-resolution Regional Ocean Modelling System (ROMS) model, which drives the transport of reactive suspended particulate organic matter in the region. The physiological cold-water coral model, with coral food uptake, assimilation, and respiration as key variables and with model parameters estimated from available experimental report, is coupled to the reactive transport model of suspended particulate organic matter. Model predictions agree with coral reef biomass and respiration observations in the study area and coral occurrences comply with predictions from previously published habitat suitability models. Cold-water coral biomass was mainly predicted on coral mounds and ridges in the area. Filter feeding activity by cold-water corals proved to strongly deplete food particles in the bottom waters. Replenishment of food particles by tidal currents was therefore vital for cold-water coral growth. This mechanistic modelling approach has the advantage over statistical and machine learning-based predictions that it can be used to obtain an understanding of the effect of changing environmental conditions such as ocean temperature, surface production export, or ocean currents on cold-water coral biomass distribution and can be applied to other study areas and/or species.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.191
Teacher spread0.183 · 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 designSimulation or modeling
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 routes2
Has abstractyes

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