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Record W2515431473 · doi:10.1155/2016/7183813

Biomonitoring Climate Change and Pollution in Marine Ecosystems: A Review on<i>Aulacomya ater</i>

2016· review· en· W2515431473 on OpenAlexaff
France Caza, Maximiliano Cledón, Yves St‐Pierre

Bibliographic record

VenueJournal of Marine Biology · 2016
Typereview
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMytilusBiomonitoringBioindicatorBlue musselMusselEcologyEcosystemClimate changeMarine ecosystemBiodiversityBiologyEnvironmental scienceIntertidal zoneFishery

Abstract

fetched live from OpenAlex

The sedentarism and wide global distribution of the blue mussel Mytilus edulis have made it a useful bioindicator to assess changes in the health status of the marine ecosystem in response to pollution and other environmental stresses. Effective biomonitoring of an ecosystem requires, however, that multiple biomarkers be used to obtain an accurate measure of the cumulative effects of different sources of environmental stress. Here, we provide a first integrated review of the biological, economical, and geographical characteristics of another species of mussels, the ribbed mussel Aulacomya ater . We discuss the use of Aulacomya ater as a complementary biomonitor to the blue mussel to assess the impact of pollutants and climate change. Recent findings have indeed shown that Mytilus edulis and Aulacomya ater have distinctive anatomy and physiology and respond differently to environmental stress. Monitoring of mixed beds containing blue and ribbed mussels may thus represent a unique opportunity to study the effect of environmental stress on the biodiversity of marine ecosystems, most notably in the Southern hemisphere, which is particularly sensitive to climate change and where both species often cohabitate in the same intertidal zones.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.046
GPT teacher head0.330
Teacher spread0.284 · 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 designOther design
Domainnot available
GenreReview

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

Citations10
Published2016
Admission routes1
Has abstractyes

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