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Insights into coastal phytoplankton variations from 1979 to 2018 derived from Ba/Ca records in scallop shells (Chlamys islandica) from a fishing ground in the northern Gulf of St. Lawrence

2022· article· en· W4296311923 on OpenAlexaff
Pierre Poitevin, Virginie Roy, Peter S. Galbraith, Gwénaëlle Chaillou

Bibliographic record

VenueMarine Environmental Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsUniversité du Québec à RimouskiFisheries and Oceans Canada
Fundersnot available
KeywordsPhytoplanktonOceanographyScallopFishingFisheryEnvironmental scienceMarine ecosystemDiatomAlgal bloomArchipelagoEcosystemEcologyNutrientGeologyBiology

Abstract

fetched live from OpenAlex

Bivalve growth is affected by phytoplankton quality and availability, but long-term, coastal environmental time series related to these parameters are often lacking. Therefore, it is crucial to develop methods to accurately quantify trends in phytoplankton dynamics over time. This would be especially important for the fished scallop beds in the northern Gulf of St. Lawrence, where landings sharply declined since the early-2000s. Over the past decade, many studies have highlighted the potential of Ba/Ca ratios in bivalve shells as an environmental proxy for phytoplankton dynamics. This study presents records of Ba/Ca ratios in 31 young Chlamys islandica shells sampled in the Mingan Archipelago from 1979 to 2018. The Ba/Ca master chronology showed a decreasing trend since 2002, which could reflect changes in local phytoplankton bloom taxonomic composition, and coincides with the aforementioned decline in scallop landings. Investigations of environmental controls on barium incorporation into the shells highlight the importance of bottom, nutrient-rich waters to support diatom production or export in this fishing area. The use of such high-resolution seasonal records extracted from bio-archives may identify essential environmental mechanisms that will then assist with the development of an ecosystem-based fishery management strategy.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0210.000

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 head0.021
GPT teacher head0.269
Teacher spread0.248 · 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.

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

Citations7
Published2022
Admission routes1
Has abstractyes

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