USO DE EFLUENTE DE PLANTA DE BIOGÁS Y MICROORGANISMOS EFICIENTES COMO BIOFERTILIZANTES EN PLANTAS DE CEBOLLA (Allium cepa L., cv. ‘Caribe-71’)
Bibliographic record
Abstract
This work evaluated the effect of the application of biogas plant effluent and efficient microorganisms (ME), as biofertilizers in onion culture (Allium cepa L, cv. ‘Caribe-71’). The experiment was carried out at a field scale (in plots of 1,5 x 8,0 m) under a Latin Square design, where four treatments were applied: I. mixture of effluent and ME 5 % (v/v); II. mixture of effluent and ME 10 % (v/v); III. mixture of effluent and ME 15 % (v/v); and IV. Control treatment: with chemical fertilizer (NPK complete formula). A total of 12 foliar applications of the biofertilizers were carried out (two before sowing and then every seven days). The indicators were determined: height of the main leaf, number of bulbs, diameter of pseudostem, diameter of bulb, number of bulbs and fresh mass of plants. The results showed that the foliar application of the biogas plant effluent and the efficient microorganisms in the form of a mixture had a positive effect on the onion culture compared to the chemical fertilization of this one, due to the contribution of nutrients and beneficial microbiota that improves soil conditions and stimulates the growth and development of the plant. This work demonstrates the possibility of incorporating organic fertilization during onion cultivation, in accordance with the principles of agroecology in the context of the necessary sustainable agricultural development
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".