Analogue Particle and Random Packing Models of Ecological Community Formation
Bibliographic record
Abstract
The apparent constancy of the near linear relationship between logarithm of a specie’s abundance in a community and their rank order led to a brief review of existing species abundance distribution (SAD) models; a proposal of three further classes of analogue models; and an evaluation of a meta data set of empirical vegetation composition data. A new class of models is proposed considered particle packing of circles (2D) or spheres (3D) as representing individuals of species which under different scenarios of arrival, growth, displacement and mortality determine community maximum packing and SAD. Besides the existing random models treating each species as an entity, two further classes are added which considered dismantling rather than assembly models, or retaining identity of taxa species through successive stages of assembly or dismantling models. In each of the models, the resulting form of the SAD was dependent on which metric of an individual (density, area, volume etc) was used as a measure of abundance. A comparison was made between the resulting 576 different models and 432 sets of 10 species and 110 sets of 5 species empirical observations. There was a wide variation in species proportions and a lack of clustering around a particular model. In those comparisons, the particle packing models of individuals were as good as the random models treating species as an entity and did offer a functional explanation of the formation of the SAD. In most models and empirical data the species ranking is done retrospectively so there will always be a high negative correlation between abundance and rank implying a need for a revaluation of the ecological significance of the relationship.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".