Evaluación de propiedades físicas, químicas e hidrológicas en suelos manejados con maíz (Zea mays) y cinco programas de fertilización, La Montañona, Chalatenango, El Salvador
Bibliographic record
Abstract
The research was conducted in the municipalities of Las Vueltas, La Laguna and Chalatenango (Guarjila and Upatoro cantons), in the region of La Montanona Commonwealth, in the department of Chalatenango, during the period from May to November 2015, within the working area of the Canada-Latin America and The Caribbean Research Exchange Grants (LAGREG) project, in eight plots cultivated with corn (Zea mays) variety H-59. Two plots were located at each site: Las Vueltas, Guarjila, Upatoro and La Laguna. Chemical analyses of the soil and its physical properties were carried out in each plot. The objective of the research was to evaluate the physical and water characteristics of the soil and the effect of five levels of fertilization on the yield of the corn crop. A statistical model of Random Blocks was used, in eight plots, with 48 repetitions, the treatments were: Treatment 1 a mixture of 8.30 grams of formula 15-15-15 plus 13.40 g of Ammonium Sulfate ; in treatment 2 the same was applied to each plant as treatment 1 plus 1.30 g of potassium chloride; In treatment 3, the doses of fertilizers were applied according to the results of the soil analyzes of each of the eight plots; In treatment 4, 2.10 g of formula 15-15-15, 3.30 g of Ammonium Sulfate and 62.50 g of Bocashi were applied to each plant; in treatment 5, 125 g of Bocashi was applied to each plant; and a control plot in which no fertilizer was applied. All the soils of the plots where the research was carried out present an optimal pH for cultivation; the texture varied between sandy loam and sandy clay loam; a density between 1.19 g/cm3 to 1.57 g/cm3. The highest maize production was obtained with treatment 4 that yielded 42.92 qq/mz (2,786.54 kg/ha) and the lowest production was with treatment 5 with 34.10 qq/mz (2,213.87 kg/ha). During the investigation, a drought was observed as part of the El Nino phenomenon.
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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".