Bibliographic record
Abstract
Abstract Stem diameter distributions underpin forest planning and inventory practice worldwide, yet permanent sample plot inventories are routinely truncated by merchantability limits and diameter caps. Truncated densities are the standard remedy, but those forms are seldom documented or supported in common software, so practitioners default to biased complete-form fits. We introduce a two-stage weighted least-squares workflow that retains the familiar complete-form implementation while recovering truncated-fit accuracy. Stage one estimates a scaling factor alongside the density parameters; stage two freezes that normaliser to deliver unbiased shape and scale estimates. Applied to fixed-area plots from Québec, Canada, the two-stage estimator tracks truncated Weibull and gamma fits across 32 species-group/cover-type combinations (root-mean-square error between one-stage truncated and two-stage complete fits: $$2\times 10^{-5}$$ 2 × 10 - 5 – $$3.7\times 10^{-2}$$ 3.7 × 10 - 2 ) and yields the lowest stage-2 AICc (small-sample Akaike information criterion) in 25 of 32 cases. The companion repository provides the tallies, scripts, and notebooks required to regenerate every figure, table, and LaTeX artefact.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".