Thai elite cassava genetic diversity was fortuitously conserved through farming with different sets of varieties
Bibliographic record
Abstract
Many in situ conservation programs have been developed to preserve plant landrace diversity and to promote its sustainable utilization, but little is known about the effectiveness of the developed programs in conserving plant genetic diversity. We investigated the effectiveness of an unregulated (i.e., unplanned or open) conservation system maintained by Thai farmers in conserving Thai elite cassava ( Manihot esculenta Crantz) varieties. Specifically, we compared genetic diversity of 266 cassava clones that were collected from 80 farms in eight provinces with 16 cassava landraces and varieties released since the 1970s through genotyping with 35 informative simple sequence repeat (SSR) markers. The SSR analysis revealed a large regional heterogeneity in cassava diversity, with a strong genetic differentiation of the assayed clones among the 80 farms (19.8 %) and across the eight provinces (11.8 %). Significant associations were also found between SSR variation and farm agro-ecological factors or some farming practices. However, there was no significant genetic differentiation (0.9 %) between the 266 farm clones and 16 reference varieties. These findings suggest that the Thai elite cassava genetic diversity was fortuitously conserved by the farmers through farming with different sets of varieties. Implications of these findings are discussed with respect to on-farm conservation of plant genetic resources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".