Number, length, area or biomass: Can there be intermediates?
Bibliographic record
Abstract
The abundance of a given species is usually expressed in terms of either the number of individuals per unit area or volume (i.e., density), or biomass. These two abundance metrics generate different results at both the statistical analysis level (e.g., comparison of means) and the ecological level (e.g., diversity comparisons). We seek here to unify different abundance metrics using the formula A = N(B/N)k/3 where A is the abundance of a given species, k represents a fractional dimension, N is the number of individuals per unit of area and B is the biomass of the sampled species. When k = 0, A is density and when k = 3, A is the biomass. A value of k = 1 would give abundance approximately proportional to the sum of the length of individuals and k = 2 would give abundance approximately proportional to the sum of their surface areas. Metrics intermediate between density, length, area and biomass are possible using non-integer values of k. Applying this methodology to ichthyological data characterized by highly variable intraspecies biomass, we examined the effect of the abundance metric on the results of a three-factor analysis of variance (depth, season and site). In some cases, differences which could not be seen with either density or biomass could be seen with intermediate metrics. We suggest that many ecological results could be usefully evaluated in terms of the effect of the fractional dimension of sampling. In some cases, such an approach could identify the optimal metric.
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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 | 0.014 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".