Bibliographic record
Abstract
Les activités illégales d’exploitation forestière et de commerce du bois figurent parmi les principales causes de dégradation des forêts dans le monde. Du Paraguay à la Sibérie, de la Thaïlande au Canada, en Afrique comme en Asie, des exploitants peu scrupuleux des lois dégradent les grands espaces forestiers.Leurs pratique ne menacent pas seulement la biodiversité des forets.Elles mettent aussi en péril les collectivités dont le mode de subsistance repose quasi exclusivement sur les ressources forestières. Pour résoudre ce problème mondial, les pays concernes doivent mettre en œuvre les lois déjà existantes et des procédures garantissant leur respect. Cet ouvrage invite a une profitable réflexion sur les moyens d’y parvenir. Apres un survol de la situation dans le monde ainsi que d’intéressantes propositions pour élaborer une réglementation des forets, il s’achève sur une recommandation qui devrait être la philosophie de toute gestion durable d’un bien : mettre un terme a la course aux profits.\n\n\n\nCoupe a blanc : activités illégales d’exploitation forestière et de commerce du bois dans les tropiques Editions du CRDI, 1999 BP 8500 Ottawa ( Ontario) \n\nCanada K1G 3H9. Fax : +1 613 563 2476\n\nE-mail : pub@idrc.ca\n\nSite web : www.idrc.ca/books/897.html\n\n150 pages ISBN 0-88936-897-X\n\nPrix : 25 $125
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.770 | 0.724 |
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".