Ratio estimation to improve estimates of top height from suboptimal samples in forest inventory plots in Québec
Bibliographic record
Abstract
Top height is one of the most useful variables provided by sample plot measurements conducted during forest inventory programs. Combined with tree age, it allows the determination of site index, a common measure of forest site productivity. Unfortunately, there are cases where the number of site trees is suboptimal; this potential problem can lead to imprecise estimates of top height and site index. We approached this problem using ratio estimation techniques and incomplete data sets from forest inventory plots in Québec. We tested different combinations of suboptimal sampling of site trees to estimate top height based on their diameters, using only 2 or 3 site trees per 0.04-ha plot, instead of 4. The results show that suboptimal sampling of site trees should not be conducted randomly, and that some combinations consistently performed better than others. Also, some combinations never outperformed others and should be avoided when estimating top height of some important tree species in Québec using suboptimal sampling of site trees. Finally, for the best combinations, we concluded that the ratio estimation technique generally provided precise and unbiased estimates of top height. Key words: top height, site index, site trees, ratio estimation, forest inventory
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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 | 0.005 | 0.020 |
| 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 | 0.000 |
| 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".