Long-term assessment of birds' extirpation from a tropical agroecosystem
Bibliographic record
Abstract
To better understand the processes that may lead to potentially avoidable extinction events, it is important to identify the influence of change in the communities at the local scale. We compiled bird lists from a field station with an artificial wetland system and a history of drastic changes in land use in the Colombian Andes. This data expands from 1974 to 2018. Our assessment uses three criteria to recognize local extinction: looking at the short-term (last 5 years) variation, a comparison between baseline and the most recent data, and the time in between. These criteria also allowed us to quantify substantial colonization and recolonization events. We found that 5.9% of previously recorded species had become extirpated, 5.2% can be considered probably extirpated, and 6.7% are possibly extirpated. Moreover, there was an important turnover in foraging guilds which implies a transition in the community's functional diversity. The loss of artificial wetlands in addition to the local afforestation plan in the mid-90s at the study site likely constitute the leading factors for the observed gains and losses of species in the agroecosystem. We highlight the importance of multicriteria assessment in community-level studies to distinguish between an apparent persistence and an actual extirpation event over the long term. Artificial wetlands and agroecosystems should be better studied as they could complement regional conservation targets.
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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 | 0.001 | 0.001 |
| 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".