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Record W4384338845 · doi:10.3390/d15070854

Macrozoobenthic Diversity along an Oxygen Gradient in the Deep Trough of the Gulf of St. Lawrence (Canada)

2023· article· en· W4384338845 on OpenAlexaboutno aff
Michael L. Zettler, Falk Pollehne

Bibliographic record

VenueDiversity · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsEstuaryPolychaeteOceanographyTrough (economics)Environmental scienceHypoxia (environmental)Oxygen saturationCrustaceanBiomass (ecology)EcologySaturation (graph theory)OxygenFisheryBiologyGeologyChemistry

Abstract

fetched live from OpenAlex

In 2015, we studied the macrozoobenthic community composition along a dissolved oxygen gradient in the deep trough of the Gulf of St. Lawrence (Canada). We sampled the seabed at nine stations using box corers (three replicates per station), starting in the outer Gulf and ending in the Lower St. Lawrence River Estuary. We found four different communities dominated by polychaetes, crustaceans, and molluscs, with the emphasis shifting from mollusc to polychaete communities as oxygen saturation decreased. Contrary to our expectations, the stations furthest upstream in the estuary with the lowest oxygen saturation levels had the highest species diversity, and also the highest density and biomass values. Key genera of the hypoxic zone included bivalves (Thyasira), cumaceans (Diastylis), amphipods (Harpinia), and polychaetes such as Ampharete, Ceratocephale, Galathowenia, and Trochochaeta. We attribute this to the stability of the environmental conditions and the absence of stress, where the constant supply of oxygen, even at low concentrations, seems to be more important than the absolute oxygen concentration.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.032
GPT teacher head0.222
Teacher spread0.190 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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