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Body size–density relationship for <i>Mytilus edulis</i> in an experimental food‐regulated situation

2000· article· en· W2039599819 on OpenAlexaff
Marianne Alunno‐Bruscia, Peter S. Petraitis, Edwin Bourget, Marcel Fréchette

Bibliographic record

VenueOikos · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsFisheries and Oceans CanadaUniversité Laval
Fundersnot available
KeywordsMytilusPopulation densityBiomass (ecology)Competition (biology)SestonBiologyPopulationAnimal scienceDry weightIntraspecific competitionEcologyAllometryBotanyPhytoplankton

Abstract

fetched live from OpenAlex

We grew mussels ( Mytilus edulis ) under two different food regimes and eight population density levels to estimate the joint effects of density and biomass on their growth and survival and to determine the shape of the biomass–density ( B–N) relationship. Mussels were reared for 22 months, between December 1994 and October 1996, in 1‐L experimental chambers supplied with natural seston. Growth in shell length, individual wet mass and ash free dry mass ( m ) decreased with decreasing food availability and increasing population density. Survival was negatively correlated with density but did not differ significantly between food regimes during the first year. Variations in concentration of available food did not alter the effects of crowding on mussels, as judged from the slopes of the body size–density curves. The general patterns exhibited by B–N curves were not consistent with expectations since we found 1) no classical competition–density (C–D) effect as reported in plants at intermediate competition levels, and 2) a slope of −0.648 for m–N curves in both food regimes, which was higher than expected from self‐thinning (ST) theory. This value does not support present food‐driven ST theory. This study introduces an unusual m–N region which combines properties of both ST and C–D effect.

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)0.000
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 score0.207
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.261
Teacher spread0.245 · 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

Citations59
Published2000
Admission routes1
Has abstractyes

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