Waterfowl abundance and diversity in relation to season, wetland characteristics and land-use in semi-arid South Africa
Bibliographic record
Abstract
We studied waterfowl abundance and diversity in relation to season (wet vs dry), wetland characteristics (vegetation and morphometrics) and land-use in a semi-arid agricultural region of South Africa to determine how waterfowl respond to various wetland characteristics, particularly those of permanent agricultural ponds.Wetlands were visited during the wet (n = 215) and dry (n = 178) seasons of 1997 and species' abundances, and wetland and upland characteristics were recorded. Canonical correspondence analyses and multiple regressions determined which wetland and upland characteristics were most strongly associated with waterfowl density and species richness for both the wet and dry season. Overall, diving ducks were not abundant in the wet season, and were rare to absent in the dry season. Divers only responded positively to the characteristics of natural wetlands, including greater surface area, percent coverage of emergent vegetation, and high (ungrazed) shoreline vegetation. Of six species of dabbling ducks present during the wet season, occurrence of three co-varied with wetland and upland characteristics associated with agriculture, namely permanent water, and agricultural grains in the dry season. Being largely grazers, geese responded positively to the higher proportions of bare shoreline, characteristically surrounding agricultural ponds. Because only a few species associated with artificial waterbodies, natural wetlands should be conserved to protect waterfowl diversity in semi-arid South Africa.
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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 | 0.000 | 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".