Can exotic tree plantations preserve the bird community of an endangered native forest in the Argentine Pampas?
Bibliographic record
Abstract
Worldwide, the areas covered by native forests are declining while those of tree plantations are increasing. This has raised the question of whether tree plantations are able to preserve native forest species. In Argentina, the main native forests of the Pampas region, called talares, are endangered and their disappearance is imminent. Although exotic tree plantations are increasing in this region, their role in maintaining native bird diversity has not been studied in Argentine Pampas. We compared the bird community attributes and vegetation structure of talares native forests with those of tree plantations. Compared with talares native forests, plantations exhibited markedly lower bird richness (up to 80% lower), and all forest-dependent bird species were absent in plantations. Talares and plantations differed also in some aspects of vegetation structure, which usually are key determinants of bird abundance. Given the extreme importance of talares for forest birds, this bird community will be deeply affected if talares native forests continue to decline, as nearby plantations do not offer suitable habitat. To maintain the bird diversity of talares, and probably the diversity of other unstudied taxa related to them, we recommend management actions that should be applied urgently in these endangered forests of the Argentine Pampas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".