Priorities for Research and Development in the Management of Pollination Services for Agricultural Development in Africa
Bibliographic record
Abstract
It is increasingly recognized that a sustainable future for agriculture must build on ecosystem services. Pollination is an important ecosystem service in all agroecosystems. In much of Africa the main challenge is conserving pollinator biodiversity in traditionally “ecologically-intensive” agroecosystems that are changing to meet different demands for food security and poverty alleviation, rather than safeguarding pollination in transition from conventional agricultural systems, with a high reliance on purchased inputs, to “ecologically-intensive” agroecosystems using natural inputs provided by biodiversity. Priority issues for research and development in pollination services in Africa include, inter alia: quantification and documentation of pollination deficits and finding measures to address these; socio-economic valuation of pollinator-friendly practices; assessment of lethal and sub-lethal effects of farming methods, such as pesticide use, on crop pollinators; identification of habitat management practices that enhance synergies between pollinator lifecycles and crop growing patterns; and policy analysis in relation to drivers and trends in pollination services and management.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".