Tree value and log product yield determination in radiata pine (<i>Pinus radiata</i>) plantations in Australia: comparisons of terrestrial laser scanning with a forest inventory system and manual measurements
Bibliographic record
Abstract
New sensor-based approaches for assessing the quantity, quality, and value of timber are being developed with the goals of improving the accuracy and economics of forest measurements. One new approach is based on terrestrial laser scanning (TLS). Thirty-three plots in six radiata pine (Pinus radiata D. Don) stands were scanned using TLS. Tree locations were automatically detected. Stem profiles were measured using three methods: (i) TLS scans, (ii) Atlas Cruiser inventory procedures, and (iii) manual measurement after harvesting. Stems were optimally bucked based on log specifications and prices for Australian markets. Tree values and log product yields were estimated for the TLS data and compared with estimates based on Cruiser and actual manual measurements of stem profiles. TLS volume and value recovery were within 8% and 7%, respectively, of actual harvester recovery for five of the six stands in which it was used. Cruiser volume and value estimates were both within 4% of actual harvester recovery. Plot preparation procedures, tree characteristics, and taper equations used to model diameters on hidden stem sections affected the accuracy of automated stem detection and profile measurements for the TLS system. Improvements in data capture and analytical procedures should improve the accuracy of TLS-based volume and value estimates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".