Species abundance distribution pattern of microarthropod communities in SW Canada.
Bibliographic record
Abstract
It is still unclear whether simple niche-derived or neutrality-derived statistical models is better to quantify the experimental species-abundance distribution pattern (SAD) for microarthropod communities. In the present study, by utilizing the sampling diversity data of three microarthropod taxonomic groups (oribatids, collembolans and mesostigmatids), my objectives are to test and compare five alternative statistical models for fitting empirical microarthropod SAD curves, including neutral, Zipf, broken stick, niche preemption and geometric models. Fitting power of the models were evaluated using test, Kolmogorov-Smirnov (KS) test and Akaike Information Criterion (AIC). my results showed that, for the SAD of the whole microarthropod community and mesostigmatid group, Zipf model is the best model identified by AIC criteria. For oribatid and collembolan SAD curves, geometric model is the most favored one. However, all the models yielded significant difference between the expected and observed SAD patterns over different taxonomic groups, as indicated by both and K-S tests. Thus, either neutral and niche models could explain SAD patterns of microarthropod communities perfectly. In summary, the synergy of different mechanisms and the development of hybrid models and the proper transformation might be of some helps to remove the observed significant difference for microarthropod communities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".