Assessment and monitoring of damage from insects in Australian eucalypt forests and commercial plantations
Bibliographic record
Abstract
Abstract This paper presents a review of recent developments in the assessment and monitoring of health in Australian eucalypt forests and plantations of pine and eucalypt species, with an emphasis on damage caused by herbivorous insects. The diverse range of interests and priorities amongst Australian stakeholders of native forests and plantations influences the scale, resolution and accuracy of results sought, and this in turn influences how the assessment data are collected, analysed and reported. The authors discuss sampling systems that include extensive ground‐based surveys, permanent plots and airborne technologies being developed in Australia. In all cases, there is an appreciation that the assessment protocols should be objective, repeatable and cost effective. Significant progress has been made in the application of digital, remotely sensed imagery to detect and classify damaged forest canopies. The success of this approach depends, in part, on a sound understanding of the progression of symptoms at the leaf, tree crown and stand scale, especially those symptoms that influence spectral reflectance behaviour.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".