Prediction of tree-size distributions and inventory variables from cumulants of canopy height distributions
Bibliographic record
Abstract
The method of predicting an unknown target probability distribution via a Gram–Charlier A-series expansion (GCAE) of a user-defined base probability function and cumulants of a known distribution of an auxiliary variable is demonstrated in two applications. Both applications concern predictions of the distribution of tree stem diameters with cumulants of airborne laser scanning (ALS) canopy heights and an index of canopy density as predictors. All predictions were generated in a leave-one-out cross-validation scheme, and statistical inference was based on 100 stochastic predictions of the tree sizes in 308 plots of 400 m2. The mean and variance of GCAE-predicted distributions were rarely significantly different from actual values, yet between 19 and 32% of the predicted GCAE distributions were significantly different from the actual distribution. The rejection rate with predictions generated from a simpler DECILE method was, on average, 2.5% lower. GCAE is still recommended due to its potential usefulness. Cumulants of ALS canopy heights are independent of plot area and effective for area-based least-squares predictions of forest inventory variables.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".