Bibliographic record
Abstract
Abstract Biological control in Argentina has a longstanding tradition with records of natural enemy introductions since the beginning of the 20th century, mainly between 1900 and 1940. Eight predators, 70 parasitoids and seven pathogens have been introduced for arthropod control, plus eight weed biocontrol agents. Argentina has also provided 22 arthropod species to Africa, Australia, Canada and the USA for arthropod pest biocontrol. At least 26 agents from Argentina have been released against 24 weeds of South American origin around the world, notably for freshwater invaders. Fruit production and pine plantations still have the largest areas under some degree of classical biocontrol, yet the current impact of the agents is not well known. Citrus fruit flies are under experimental augmentative biocontrol with one parasitoid species and 1 million hectares under an IPM regime that includes cultural control, trapping and SIT technology. As for private initiatives, a small fraction of greenhouse tomatoes and peppers are under augmentative biocontrol. Augmentative releases are also made in sugarcane plantations and citrus groves. Finally, an extensive part of the Argentine territory is affected by thistles and skeleton weed, and several artificial and natural water bodies invaded by native aquatics are subject to classical biocontrol, although they are still important weeds in many areas. Despite the government's explicit endorsement, resources are scarce and applied biocontrol in all its forms is still sorely undeveloped in Argentina.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.009 |
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".