Increasing the effectiveness of knowledge transfer activities and training of the forestry workforce with marteloscopes
Bibliographic record
Abstract
Sample plots of various sizes and forms are put in place to describe and monitor trees, stands or forest characteristics. The intent is usually to provide the basis for measuring and understanding the forest. Marteloscopes, by contrast, are large plots designed for tree marking simulations, set up with human beings as the main focus: they are used for knowledge transfer activities, training of various categories of forestry workers, and even for the study of human tree selection behaviors. This distinctive type of permanent plot is relatively new and unfamiliar to North America’s forestry professionals. In this paper, we provide a working definition of marteloscopes and demonstrate how they can significantly improve knowledge exchange and learning experiences, notably for complex decisions on partial cutting treatments. Potential uses of marteloscopes, their benefits as well as some of the challenges they bring are discussed in the presentation of selected examples from Canada, the United States and Italy. These examples cover uses by research agencies, universities and nonprofit organizations. Finally, we discuss ongoing developments for marteloscopes, the standardization of protocols and the potential benefits of linking marteloscopes into an international network, as more of them are put in place in diverse and unique forest settings.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".