Bibliographic record
Abstract
In Vanuatu, a decade of low world cocoa prices has led to many smallholder farmers neglecting their cocoa trees, resulting in infestation by black pod disease and rats, with catastrophic yield losses.Attempts by the government and other development organisations to encourage improved cocoa management, undertaken since the early 2000s, have had little impact, with few farmers ready to dedicate additional time to cocoa production.As a result, despite such programmes, cocoa yields have continued to fall, even while prices for cocoa on world markets have begun to rise.In 2011, an ACIAR-funded project, involving the Secretariat of the Pacific Community (SPC), CABI and the Commonwealth Scientific and Industrial Research Organisation (CSIRO), investigated how labour constraints impacted on cocoa management.In a survey conducted by SPC, the most common reason preventing farmers from adopting recommended practices such as pruning and weeding was lack of time, due to competition from copra production (another cash crop), food production and village activities.Based on this information, an Integrated Pest and Disease Management (IPDM) programme was initiated by CABI and CSIRO to demonstrate that increased yields could be achieved by using straightforward and practical control methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".