Developing methods for pre-harvest inventories which use a harvester as the sampling tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Mechanised harvesters, with their measurement technologies and on-board computer power, provide an opportunity to augment, or in some cases replace, traditional pre-harvest inventory systems and to reduce inventory costs. A brief review of the literature on the tree measurement errors associated with pre-harvest inventory systems indicates that they may be no less than errors found in recent studies of harvester measurements in New Zealand, the United States (Oregon) and Canada. Developmental trials of harvester-based inventory systems in Australia indicate that using a harvester to destructively sub-sample could provide reasonable estimates of volume and log grade provided that it has the same in-built assumptions and optimisation systems as the harvester undertaking the actual harvest. Greater than 92% of predicted value recovery could be obtained. Further work is required on such topics as sampling systems, area determination, costs and operating procedures.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it