Local working collections as the foundation for an integrated conservation of Theobroma cacao L. in Latin America
Bibliographic record
Abstract
The intraspecific diversity of cacao has traditionally been preserved in genebanks. However, these establishments face various challenges, notably insufficient funding, accession redundancy, misidentification and lack of wild cacao population samples. In natural environments, it is expected that unknown varieties of cacao may still be found, but wild populations of cacao are increasingly threatened by climate change, deforestation, habitat loss, land use changes and poor knowledge. Farmers also retain diversity, but on-farm conservation is affected by geopolitical, economic, management and cultural issues, that are influenced at multiple scales, from the household to the international market. Taking separately, ex situ , in situ and on-farm conservation have not achieved adequate conservation fostering the inclusion of all stakeholders and the broad use of cacao diversity. We analyze the use of the traditional conservation strategies ( ex situ , in situ and on-farm) and propose an integrated approach based on local working collections to secure cacao diversity in the long term. We argue that national conservation networks should be implemented in countries of origin to simultaneously maximize alpha (diversity held in any given working collection), beta (the change in diversity between working collections in different regions) and gamma diversity (overall diversity in a country).
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".