Bacterias endófitas de la raíz en líneas de maíces tolerantes y susceptibles a sequía
Bibliographic record
Abstract
El maíz (Zea mays) ocupa el segundo lugar como alimento en el mundo y la sequía limita su productividad. Las plantas albergan bacterias endófitas que influyen en la sanidad y tolerancia a la sequía. El objetivo de esta investigación fue estimar la densidad y diversidad de las bacterias endófitas cultivables de la raíz en siete líneas homocigóticas de maíces tolerantes y siete susceptibles a sequía en tres localidades de México durante tres ciclos del cultivo. La densidad y diversidad de las poblaciones bacterianas se evaluó mediante conteo directo en placas y se identificaron por PCR. Los resultados identificaron tres grupos de bacterias endófitas: 1) altamente frecuentes (Bacillus subtilis, Bacillus megaterium y Pseudomonas geniculata), 2) frecuentes (Bacillus firmus, Pseudomonas hibiscola y Sinorhizobium meliloti) y 3) baja frecuencia (Acinetobacter soli, Stenotrophomonas maltophila y Burkholderia gladioli. El análisis de varianza (ANOVA) mostró diferencias significativas (p?0,05) en la densidad (Log10 UFC g-1 de raíz) de población por localidad, ciclo de cultivo, días después de siembra y líneas de maíz. La densidad de Bacillus subtilis, Pseudomonas hibiscola en la localidad de El Batán y Bacillus megaterium, Sinorhizobium meliloti en Tlaltizapán, fueron significativamente mayores en las líneas de maíz tolerantes que en las susceptibles a sequía.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".