Effects of livestock grazing on flocks of seed-eating birds in the central Monte desert, Argentina
Bibliographic record
Abstract
Animal populations often decline due to habitat disturbance, but the initial response of organisms to human-induced environmental change is usually behavioral. Intra- and inter-specific interactions can restrict or facilitate access to resources, resulting in changes to individual fitness, and resource depletion may affect the frequency and strength of interactions. In birds, it is often assumed that feeding in groups increases foraging efficiency. We assessed how the reduction of seed resources provoked by cattle grazing affected different properties of seed-eating bird flocks in woodlands having the same structural characteristics but differing in seed abundance. Under lower availability of grass seeds (i.e., under grazing), flocks were smaller and less rich and birds showed a lower flocking propensity. This pattern could be explained by three non-exclusive hypotheses. Food reduction caused by grazing (i) decreases the number of seed-eating birds and concomitantly generates smaller flocks; (ii) reduces the density of nuclear species, decreasing the group cohesion in large flocks; (iii) makes large flocks less attractive by increasing individual competence for food. Our results provide evidence that cattle grazing affect the interactions of seed-eating birds and suggest the importance of understanding flocking behavior to bring about management actions.
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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 | 0.000 | 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".