Bibliographic record
Abstract
The area of occupancy of a species and its abundance are dependent on the spatial scale at which they are measured. However, it is less obvious how the scale of sampling affects their correlation. This study investigated and modeled the effects of sampling unit size and a real extent on the interspecific occupancy‐abundance relationships for a tropical tree species assemblage at a local scale and a temperate bird species assemblage at a regional scale. The results showed that both sampling unit size and study extent had profound quantitative effects on the occupancy‐abundance relationship, although it remained positive. Several properties of the occupancy‐abundance relationship can result from the effects of scale: 1) the linearity of the relationship decreases with the increase of sampling unit size; 2) for a given abundance, the area of occupancy increases with sampling unit size; and 3) variation in the area of occupancy increases with the increase of both sampling unit size and extent, and if the extent is large enough may be sufficient that no occupancy‐abundance relationship is observed. Although the occupancy‐abundance relationship can be satisfactorily modeled, the parameters depend on the scale used. This suggests that a model derived from one scale cannot be applied to another. In other words, to estimate the rarity or commonness of species using such a model, the estimation must be strictly done using the same sampling scale for all the species.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".