OCCURRENCE OF WATERFOWL AND SHOREBIRDS IN RICE CULTIVATIONS IN THE NORTHWESTERN REGION OF PARANÁ STATE, BRAZIL
Bibliographic record
Abstract
A significant portion of the world's natural wetlands has been lost or altered, primarily due to agricultural expansion. Among these activities, rice cultivation stands out, particularly in the southern Brazilian states. Rice, a grass adapted to aquatic environments, is typically grown in flood-prone areas, creating conditions similar to natural wetlands and providing habitat and food resources for many waterbird species. In the state of Paraná, natural environments suitable for sustaining large concentrations of waterbirds are scarce. The first records of large waterbird flocks in the region were made along the left bank of the Paraná River, in artificial lagoons formed for rice cultivation. Given the ecological and economic relevance of this issue, a systematic study was conducted to assess the species richness and abundance of birds benefiting from these conditions. The research was carried out in floodplain rice fields along the right bank of the Ivaí River, a tributary of the Paraná River, within the municipalities of Planaltina do Paraná, Querência do Norte and Santa Mônica. The study began in 2013 and extended through 2019. Additional censuses were conducted in 2021 and 2024, totaling 20 field phases. Across the three study sites, 14 families and 49 species of water-associated birds were recorded. The findings provide valuable insights for developing agricultural land management strategies that maintain the functionality of these areas as supplementary habitats while balancing agricultural productivity with waterbird conservation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".