Feasibility of estimating total stem volume and aboveground biomass from measurement on the largest trees in even-aged pure stands
Bibliographic record
Abstract
Feasibility was tested of estimating the total stem volume and aboveground biomass from data of only the largest trees in even-aged pure stands. We applied a method of fitting a size-distribution function to data that exclude information of smaller individuals in a stand and compared the predicted stem volume and aboveground biomass with those calculated with data of all living trees in the stand. The paired t test showed that the predicted values of the total stem volume and aboveground biomass were not different (p = 0.05) from those observed even if only the largest 10% of the trees were used for estimation with the 3/2 power distribution. Results were similar with the beta-type distribution; however, data from at least the largest 30% of the trees in the stand must be included. Absolute values of the relative error of the predicted total stem volume or aboveground biomass were generally in the range 1020%, indicating that the present method is accurate enough to be used for calculation of these variables. However, there is systematic bias in the predictions of the total stem volume and aboveground biomass of a stand. Possible causes of the indicated biases and potential ways for improvement of the predictions were discussed.
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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 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".