Estado situacional e identificación de oportunidades y su relación con las prácticas de eco eficiencia en el Instituto Nacional de Innovación Agraria E.E.A. “San Roque” San Juan Maynas 2023
Bibliographic record
Abstract
The general objective of this research was to evaluate the current situation, identify opportunities for improvement, and promote eco-efficiency practices in the administrative areas of the National Institute of Agricultural Innovation (INIA), specifically at the San Roque Experimental Station in San Juan, Maynas, during 2023. The study was quantitative, descriptive, and non- experimental in design. The target population of the study was the administrative staff of said institution, and the sample consisted of 50 workers. Inclusion and exclusion criteria were considered; the techniques were documentary review and surveys, and the instrument was a questionnaire validated by the Ministry of Agriculture, Livestock, and Livestock (MINAM). The data were analyzed, processed, and tabulated using SPSS v27 and Python v3.11.2. The results indicate variability in energy consumption at INIA San Roque, revealing clear opportunities for improvement during the last quarter of the year. Furthermore, a 21.3% increase in diesel consumption in the last quarter reveals an operational dependence on heavy machinery and poor energy use planning. INIA San Roque's administrative staff applies certain basic eco-efficiency practices, such as turning off lighting, reducing paper use, and partially reusing materials. However, these actions lack continuity, monitoring, and institutional support, limiting the sustainability of these good practices, which currently depend more on individual initiative than on organizational policy.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".