Estimación de la huella de carbono en parcelas de quinua orgánica en el sur de Bolivia: Estudio de caso
Bibliographic record
Abstract
This case study is part of the research project "Creating Indigenous Women's Green Jobs under Low-Carbon COVID-19 Response and Recovery in the Bolivian Quinoa Sector). I estimate the carbon footprint associated with the quinoa production in southern Bolivia based on primary information of 19 plots. Using a cradle-to-gate life cycle analysis approach, under the ISO 14067 standard, and analyzing various emission sources through the Cool Farm Tool, I determine that the carbon footprint generates an average of 741 .7 kg CO2e per plot; and an average of 267.4 kg CO2e per hectare. The main emission sources identified are the use of organic fertilizer (54%), the consumption of fossil fuels (35%) and the use of protection inputs (8%). Considering the declared unit of 1 kg of harvested quinoa, I obtain the carbon footprint results per product, with values ranging between 0.3 and 2.3 kg CO2e/kg of quinoa and an average of 0.98 kg CO2e/ kg of quinoa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".