Energetic Carrying Capacity of Actively and Passively Managed Wetlands for Migrating Ducks in Ohio
Bibliographic record
Abstract
ABSTRACT Habitat conservation strategies of the North American Waterfowl Management Plan (NAWMP) are guided by current understanding of factors that limit growth of waterfowl populations. The 1998 implementation plan of the Upper Mississippi River and Great Lakes Region Joint Venture (UMR and GLRJV) assumed that availability of foraging resources during autumn in wetlands actively managed for waterfowl was the primary limiting factor for duck populations during the nonbreeding season. We used multistage sampling during autumn and spring 2001–2004 to estimate energetic carrying capacity (ECC) of actively and passively managed wetlands in Ohio, USA, and examine this assumption. Energetic carrying capacity during autumn was similar between actively and passively managed wetlands each year. Averaged across years, energetic carrying capacity was 3,446 and 2,047 duck energy‐days (DED)/ha for actively and passively managed wetlands, respectively. These estimates exceeded the UMR and GLRJV assumption that 1,236 DED/ha were provided by managed wetland habitats. Energetic carrying capacity declined each year by >80% between autumn and spring migration. Consequently, ECC of actively and passively managed wetlands was low during spring ( = 66–242 DED/ha). These results suggested that duck foraging resources in actively and passively managed wetland habitats are abundant during autumn, but overwinter declines may create food‐limiting environments during spring.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".