Plagas entomológicas en cultivos de espárrago (Asparagus officinalis L.) en el Perú: identificación taxonómica mediante morfología y el código de barras de ADN
Bibliographic record
Abstract
Identifica insectos plaga del esparrago mediante morfologia y codigo de barras de ADN. Para la recolecta de insectos se coloco una trampa de luz, bandejas amarillas y se realizo busqueda directa en los campos de esparrago. Luego, las muestras fueron procesadas en el laboratorio e identificadas previamente con morfologia y la caracterizacion molecular fue realizada en el Instituto de Biodiversidad de Ontario (BIO); posteriormente las secuencias fueron identificadas en el BOLD System. Se identificaron morfologicamente 13 especies de adultos a partir de 111 especimenes correspondientes a nueve especies de Lepidoptera (Noctuidae y Pyralidae) y cuatro especies de Scarabaeidae (Coleoptera). Las identificaciones de las secuencias de ADN fueron exitosas para el orden Lepidoptera, coincidiendo con siete especies identificadas morfologicamente y representando el 77,7% de exito. Para Coleoptera no se obtuvieron coincidencias. Por lo tanto, el sistema de codigo de barras de ADN es una herramienta que permite asignar secuencias solo cuando previamente exista una biblioteca de referencia con la cual se pueda comparar.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".