Body size, population density and factors regulating suspension-cultured blue mussel (
Bibliographic record
Abstract
We sampled 27 month-old mussel populations grown on collector ropes in Cascapédia Bay, Quebec, to test whether density-dependent growth was present concomitantly to self-thinning, a process which was previously shown to occur in this system and thought to be driven by spatial constraints. Biomass-density curves of raw samples were curvilinear, suggesting density-dependent growth. However, at least two cohorts were present. Fractionating the samples on the basis of age yielded a linear relationship for the main, 2 year-old cohort. This implies density-independent growth and rules out food regulation in these populations. Therefore, our results are consistent with inferences drawn previously from the values of the self-thinning exponent, that is, space-regulated self-thinning. Our results suggest that curvilinearity of the raw biomass-density curves resulted from a bias caused by including the 1 year-old cohort and spat of the year in the analysis. This conclusion is supported by a model showing that samples with mixed cohorts can yield linear, concave or convex biomass-density curves without density-dependent growth. The shape of the curves depends on the scaling relationships between cohort abundances. It appears that the shape of biomass-density curves may be a useful complementary criterion – in addition to the value of self-thinning exponents – for the identification of food or space as factors regulating cultured populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".