IUFRO News 2013/8: Urgent Need for Reliable Methods of Virus Detection and Identification in Forest Trees
Bibliographic record
Abstract
The topics covered in this issue range from viruses and phytoplasma of forest and urban trees to forest genetics, silviculture and ungulates, and legal aspects for sustainable forest development... Learn about the topics discussed at recent IUFRO meetings, such as the IUFRO Working Party 7.02.04 conference on "Viruses and Phytoplasma of Forest and Urban Trees" in Berlin, Germany; the Forest Genetics 2013 conference in Whistler, Canada, involving IUFRO Working Party 2.02.05; the roundtable discussion on the joint management of grazing ungulates and forest ecosystems in Minneapolis, USA, with the participation of IUFRO Working Party 1.01.12; or the 15th International Symposium of IUFRO Research Group 9.06.00 Forest Law and Environmental Legislation in Tirana, Albania. And, keep informed about upcoming meetings, open positions, new publications and more!
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.030 | 0.019 |
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".