Bibliographic record
Abstract
Summary 1. An important question in macroecology is: Can we estimate a species’ abundance from its occurrence on landscape? Answers to this question are useful for estimating population size from more easily acquired distribution data and for understanding the macroecological occupancy–abundance relationship. 2. Several methods have recently been developed to address this question, but no method is general enough to provide a common solution to all species because of the wide variation in spatial distribution of species. 3. In this study, we developed a mixed Gamma‐Poisson model that generalizes the negative binomial model and can characterize spatial dependence in the abundance distribution across cells. Under this framework, without any extra information, the clumping parameter and species abundance can be estimated using a map aggregation technique. This model was tested using a set of empirical census data consisting of 299 tree species from a 50‐ha stem‐mapped plot of Panama. 4. A comparison showed that the new method outperformed the previous methods to an appreciable degree. Particularly for abundant species in a finely gridded map (5 × 5 m), its bias is very small and the method can also reduce the root mean square error up to 30%. Like for previous methods, however, the new method’s performance decreases with the increase in cell size. 5. As a by‐product, the new method provides an approach to estimate spatial autocorrelation of species distribution which is otherwise difficult to estimate for presence/absence map.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".