Quality of Roof-Harvested Rainwater for Irrigation of Crops by Family Farmers in Brazil
Bibliographic record
Abstract
The efficiency of family farmers irrigation is conditioned by several factors such as good quality water supply. In this context, it was to evaluate the salinity and physical, chemical, and bacteriological characteristics of water harvested from roofs for irrigation of crops by family farmers in Brazil. The experiment was conducted at the Federal Institute Goiano, in Rio Verde, Goiás, Brazil. The rainwater collected on the roofs was sent through vertical and horizontal conductors to the storage tank, which has a capacity of 5000 L. The risk of salinity, pH, turbidity, conductivity, color, total dissolved solids and thermotolerant coliforms during 13 precipitation events was analyzed. Rainwater was classified as water with no salinity problem (EC < 0.7). The variation of the electrical conductivity (EC) and Total Dissolved Solids (TDS) were similar during the collection period, with mean values of 0.05 dS m-1 and 29.72 mg L-1 for EC and TDS, respectively. The pH of the rainwater presented little variation, presenting a mean of 7.37. The mean value of the color of rainwater was 11.45 PtCo L-1, while the turbidity averaged 4.89 UT. During the period of rainwater harvesting, it was observed absence of thermotolerant coliforms in all samples. It is concluded that rainwater does not present restrictions on the risk of salinity and that all physical-chemical and bacteriological parameters analyzed are within the limits allowed by Resolution 357/2005 of CONAMA, indicating the propensity of this type of water for irrigation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".