A method for updating variable radius plot surveys
Bibliographic record
Abstract
Variable radius plot surveys can require updated estimates when the quality of previous estimates have declined over time. We developed a statistical estimator for this purpose that is the product of two estimators, one estimator is for the updated volume-to-basal-area ratio and the other estimator is for the updated basal area. Our estimator relies only on the surveyor identifying previously measured trees and does not require the identification of ingrowth, which simplifies its application. We also propose a variance estimator that relies on the bootstrap method. We investigate the point and variance estimators under repeated sampling of stem-mapped populations that include five measurements over a period of approximately 20 years. Based on 144 sampling scenarios, our estimator demonstrated relative biases ranging between −0.3% and 0.8%. The bootstrap variance estimator tended to be deflated, with relative absolute biases ranging between −8% and 4%. When revisiting one third of the initially installed plots, our estimator obtained relative root mean square errors within 1.6% of an independent resurvey effort that does not use the prior survey. This implies that our estimator can substantially reduce field work while producing surveys of approximately the same quality when updating prior surveys.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".