Bibliographic record
Abstract
Fixed-count sampling (plotless) remains attractive for forest inventories in difficult terrains and for their control of the number (k) of trees to measure. Although recent fixed-count estimators of density (PDE) are less biased than older ones, the risk of a nontrivial bias remains a deterrent. A recently published PDE based on a generic algorithm for predicting distances to the k + m nearest tree (m = 1, 2, ...) has attractive properties in terms of average bias and average root mean squared errors across a wide spectrum of spatial point patterns. However, the risk of a sizeable bias remains an issue. Sensitivity to spatial patterns is seen as its main weakness. It is hypothesized that a new PDE with robust properties will mitigate the bias issue and encourage wider use. To this end, a new PDE estimator is proposed. It builds on a mixture of observed and predicted distances to a set of k + m nearest trees to generate counts of actual and virtual trees inside a circle with a data-driven fixed radius. The proposed new robust fixed-count density estimator achieved an average absolute bias of 1.2% when tested across a wide range of point patterns (54 actual and four simulated). The maximum absolute bias was 4.4%, a significant reduction when compared with otherwise attractive alternative PDEs. Root mean squared errors and coverage of 95% confidence intervals were also encouraging. The deterrent bias issue in PDEs has been sharply reduced with the proposed estimator.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".