Optimizing efficiency of height modeling for extensive forest inventories
Bibliographic record
Abstract
Although critical to monitoring forest ecosystems, inventories are expensive. This paper presents a generalizable method for using an integer programming model to examine tradeoffs between cost and estimation error for alternative measurement strategies in forest inventories. The method is applied to an example problem of choosing alternative height-modeling strategies for 1389 plots inventoried by field crews traveling within an 82.5 × 106 ha region of the west coast of North America during one field season. In the first part of the application, nonlinear regional height models were constructed for 38 common species using a development data set of 137 374 measured tree heights, with root mean square error varying from 6.7 to 2.1 m. In the second part of the application, alternative measurement strategies were examined using a minimal cost objective subject to constraints on travel time and estimation error. Reduced travel time for field crews can be a significant portion of the cost savings from modeling tree heights. The optimization model was used to identify a height-modeling strategy that, given assumptions made, resulted in <10% of maximum average plot volume error, >33% of potential measurement cost savings, and small bias for estimates of regional volume and associated sampling error (0.1% and 0.4%, respectively).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".