The Amazon Initiative: A multidisciplinary, international consortium for prevention, mitigation and reduction of resource degradation
Bibliographic record
Abstract
An institutional consortium has been formed in the Amazon to implement collaborative programs to reverse resource degradation through sustainable land use systems. The Amazon Initiative (AI) Consortium for Conservation and Sustainable Use of Natural Resources was created in mid-2003 and formalized in 2004 by the National Agricultural Research Institutions of Bolivia, Brazil, Colombia, Ecuador, Peru and Venezuela, together with four centers of the Consultative Group on International Agricultural Research: CIAT, CIFOR, ICRAF, and IPGRI. Associate institutions include regional research centers and universities. Consortium partners are creating an inter-institutional and interdisciplinary team, which will function as a "distributed network" of scientists, working at different sites in the Amazon. Under this arrangement, the Amazon Initiative will create conditions for institutional articulation and strengthen analytical skills to identify priorities for research and development intervention. In addition, the AI will develop methodological tools and information communication mechanisms to enhance the role of local agents for the sustainable development of their regions. In doing so, the AI will contribute to enhancing living conditions of traditional populations and smallholders in the region, while effectively contributing to integrated natural resource management and conservation efforts in sites highly exposed to development pressures in six Amazonian countries. Key words: interdisciplinary research, land degradation, international scientific collaboration, public policy, intervention strategy
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".