Irrigation as Strengthening of Smallholder in the Municipality of Salto do Lontra
Bibliographic record
Abstract
The irrigation technology for smallholder agriculture is an important instrument to increasing both productivity and income. However for the incorporation of this technology to succeed into small properties, there is a need for interlocution among the different spheres of coordination of these economic agents. In this way, the main objective of this work was to analyze the participation of the institutional and organizational environments of the district of Salto do Lontra, located in the southwestern region of Parana state, in both the dissemination and strengthening of irrigation. This region has the highest concentration of smallholders in the state, hence its relevance. A detailed analysis of the organizational environment was made, in which interviews to the representatives of the organizations werw inserted in the management process of this sector. In order to identify the efficiency of organizations and the accessibility of public credit policies, questionnaires were applied to 35 irrigating smallholders, considering local customs, values and skills. The results demonstrated that the credit policy obtained by the public authority corroborates for the permanence of this economic and social agent in the field. However, organizations work individually, without synchronizing the needs of the category. On the other hand highlighting the universities, which proved to be strong disseminators of irrigation technology locally.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".