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Record W7135058449 · doi:10.5376/ijmec.2025.15.0017

Ecological Succession and Community Dynamics at Whale Fall Sites

2025· article· W7135058449 on OpenAlexvenueno aff
Manman Li

Bibliographic record

VenueInternational Journal of Molecular Ecology and Conservation · 2025
Typearticle
Language
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsnot available
Fundersnot available
KeywordsEcological successionWhaleEcosystemBiodiversityEnergy flowAdaptation (eye)

Abstract

fetched live from OpenAlex

This study reviews the research progress in whale fall ecology in recent years, focusing on the definition and discovery history of whale falls, the division of ecological succession stages, the dynamic driving mechanism of communities, and the connection between whale falls and other deep-sea ecosystems (cold seep, hydrothermal). It also compares the similarities and differences among whale fall communities in different sea areas and whale species. Research shows that whale falls, as unique "nutrient islands" in the deep sea, have nurtured rich and specialized biological communities, and their succession process reflects complex interspecific interactions and energy flow mechanisms. The decomposition process of whale carcasses releases a huge amount of organic matter, triggering continuous ecological succession stages, including the scavenging stage, the eutrophic opportunism stage, the sulfide-driven stage, and the oligotrophic "reef" stage. The species composition and functional dynamics vary in each stage. Whale fall ecosystems play a significant role in maintaining deep-sea biodiversity, promoting the cycle of energy and matter, and connecting scattered chemical energy ecological hotspots. In-depth research on the dynamics of whale fall communities not only helps to understand the evolution and adaptation strategies of deep-sea life, but also facilitates the assessment of the role of whale falls in the carbon cycle and deep-sea ecological functions, providing a scientific basis for the conservation of deep-sea biodiversity and resource management.

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)0.002
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 score0.022
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.280
Teacher spread0.263 · 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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