IUFRO News 2012/3: New IUFRO-FORNESSA Research Networking Initiative in Africa
Bibliographic record
Abstract
Learn about a new IUFRO-FORNESSA initiative addressing deforestation and forest rehabilitation in Africa and read an interview with IUFRO's Forest Policy and Economics Division Coordinator. This issue informs about a new IUFRO-FORNESSA research networking initiative that addresses the challenges of deforestation and forest rehabilitation in Africa. It also presents an interview with Daniela Kleinschmit, Coordinator of IUFRO Division 9 "Forest Policy and Economics", who is heading the unit on forest policy research at the Swedish University of Agricultural Sciences (SLU). Also read about Professor Don Koo Lee's retirement from Seoul National University and, the Honorary Professorship that the Chinese Academy of Forestry has awarded to IUFRO President Niels Elers Koch. In the publications section you will learn about the new WFSE policy brief "Making Boreal Forests Work for People and Nature".
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.109 | 0.015 |
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".