Socio-environmental mapping for the prediction of aquaculture success of Pacu (<i>Colossoma</i> spp.<i>, Piaractus</i> spp., and hybrids) in the Bolivian Amazon
Bibliographic record
Abstract
Tropical aquaculture has great potential to contribute to Bolivia's food security and rural livelihoods. However, despite substantial development in neighboring countries, growth of the sector has been slow and intermittent in Bolivia. One of the key limitations to effective growth is an inadequate knowledge of the aquaculture potential for its expansion. The development of a predictive tool for aquaculture propensity in the Bolivian Amazon (708,482.4 km2) for pond culture of native “pacu” (Colossoma macropomum, Piaractus spp., and their hybrids) is described. This tool includes environmental variables (water availability, temperature, flooding, and soil type) and accessibility variables (market, food and fingerling suppliers, technical assistance), that were assigned weights and thresholds through advice from experts and producers to create suitability levels pacu fish culture. Spatial modeling generated a raster map of 900 m resolution, mapping specific suitability levels. The resulting suitability map was subjected to a sensitivity analysis, to check for undue influence of individual variables. Finally, the predictive map was compared to actual fish pond distribution, resulting in an accuracy of 85.6%. This validation process indicates that the resulting tool can be used with confidence in identifying promising areas for pacu aquaculture in the Bolivian Amazon. The model can also be refined in the future with new variables as these become available with new research, such as predictions of economic performance.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".