Distance‐based methods for estimating density of nonrandomly distributed populations
Bibliographic record
Abstract
Population density is the most basic ecological parameter for understanding population dynamics and biological conservation. Distance-based methods (or plotless methods) are considered as a more efficient but less robust approach than quadrat-based counting methods in estimating plant population density. The low robustness of distance-based methods mainly arises from the oversimplistic assumption of completely spatially random (CSR) distribution of a population in the conventional distance-based methods for estimating density of non-CSR populations in natural communities. In this study we derived two methods to improve on density estimation for plant populations of non-CSR distribution. The first method modified an existing composite estimator to correct for the long-recognized bias associated with that estimator. The second method was derived from the negative binomial distribution (NBD) that directly deals with aggregation in the distribution of a species. The performance of these estimators was tested and compared against various distance-based estimators by both simulation and empirical data of three large-scale stem-mapped forests. Results showed that the NBD point-to-tree distance estimator has the best and most consistent performance across populations with vastly different spatial distributions. This estimator offers a simple, efficient and robust method for estimating density for empirical populations of plant 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".