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Record W6888777344 · doi:10.21411/cbm.a.9e0e5abf

Diversity and vertical distribution of nematode assemblages the Saguenay fjord (Quebec, Canada) environmental

2001· article· en· W6888777344 on OpenAlexaboutno aff

Bibliographic record

VenueStation Biologique de Roscoff · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsnot available
Fundersnot available
KeywordsFjordAbundance (ecology)Biomass (ecology)SedimentEcological successionDiversity indexRelative species abundanceTaxonCommunity structure

Abstract

fetched live from OpenAlex

Three stations were sampled for a meiofaunal survey in the Saguenay fjord's inner basin (Quebec, Canada). Nematodes were the dominant taxon accounting for 90 % of total individuals. Twenty nematode families and 55 genera were found. Cluster analysis showed two different depth-segregated nematode assemblages, one in the surface layer (0-2 cm) and the other in the subsurface layer (2-10 cm) of the sediment column. A comparison between abundance and biomass vertical distributions showed that smaller individuals were dominant in the surface sediment layer, while larger individuals dominated in subsurface sediment. Selective deposit-feeders were the most abundant feeding group, while epigrowth-feeders were dominant in terms of biomass. Diversity tended to decrease in downstream areas of the fjord. Species composition and size spectra at the three stations suggest the succession of different meiofaunal communities along the fjord. Abundance- biomass comparison (ABC) plots suggested that upstream areas were moderately disturbed. The difference between Shannon's index in terms of abundance and biomass (H'δ = H'abund - H'biom) is suggested to be a useful tool in detecting environmental disturbance.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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 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
Published2001
Admission routes1
Has abstractyes

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