Conservación de la Biodiversidad: Un caso en Ecuador y China
Bibliographic record
Abstract
La conservación de la biodiversidad es de interés para toda la humanidad ya que el planeta Tierra posee una gran diversidad de organismos vivos. A la vez es de importancia fundamental para mantener la estabilidad ambiental y mejorar la calidad de vida. Los países de Ecuador y China, son unos de los 17 países megadiversos del mundo que albergan un mayor índice de biodiversidad tanto de flora y fauna. Que a la vez también presenta diferentes amenazas en su biodiversidad, que es la afectación de las relaciones funcionales entre las especies de un ecosistema. Además, el cual realizan la conservación de la biodiversidad, a base de 2 enfoques de conservación in situ y ex situ. Hay que recalcar que gracias a la conservación garantiza el mantenimiento de la biodiversidad y a la vez avanzar con los sistemas de gestión para cultivar una actitud ambientalista frente a la conservación de la biodiversidad.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".