True metabolizable energy of submersed aquatic vegetation in semi-permanent marshes for dabbling ducks in the Upper Midwest
Bibliographic record
Abstract
We completed 186 TME assays with mallards (n = 72) and gadwall (n = 114) between 2015 and 2018. Bioavailable energy (i.e., TME) of submersed aquatic vegetation was highly variable among duck and vegetation species as supported by their interaction in the most parsimonious model (wi = 0.42; R2m = 0.33, R2c = 0.47). For gadwall, mean TME (± SE; kcal/g[dry]) was greatest for Eurasian watermilfoil (0.77 ± 0.32, n = 19), followed by Canadian waterweed (0.70 ± 0.31, n = 20), coontail (0.55 ± 0.28, n = 18), southern naiad (–0.61 ± 0.34, n = 17), wild celery (–0.98 ± 0.39, n = 20), sago pondweed (–1.07 ± 0.33, n = 20). Mallards assimilated greatest energy from Canadian waterweed (1.66 ± 0.26, n = 13), followed by coontail (1.51 ± 0.28, n = 8), southern naiad (1.37 ± 0.39, n = 14), sago pondweed (0.50 ± 0.22, n = 14), wild celery (0.05 ± 0.42, n = 11), and Eurasian watermilfoil (–0.13 ± 0.26, n = 12). There was evidence from a competing model (wi = 0.11; R2m = 0.34, R2c = 0.47) that females acquired 0.52 kcal/g (± 0.34) more energy from vegetation than males and that TME declined 0.20 kcal/g (± 0.16) for every 100 g increase in initial mass. \nWe estimated energetic carrying capacity at 318 random points within 20 wetlands across three years (2015–2017) in the Midwest, USA (Simpson et al. 2017). Across all points, Ceratophyllum spp. was the most commonly encountered genera (n = 188) of submersed aquatic vegetation, followed by Myriophyllum spp. (n = 64), Najas spp. (n = 47), Elodea spp. (n = 41), and Stuckenia spp. (n = 36). These five genera comprised 91.5% of the total energy density across all points and years. Extrapolated energy density estimated at sample points ranged from 0 to 5,624 EUD/ha (𝑥𝑥̅ = 426 ± 52) and biomass estimates ranged from 0 to 2,340 kg/ha (dry) (𝑥𝑥̅ = 204 ± 22). Evidenced from the best supported model (wi = 0.68; R2m = 0.19, R2c = 0.27), energy density was 195 EUD/ha (85% CI = 39 – 964) greater at managed points than unmanaged points, decreased 2 EUD/ha (85% CI = 1 – 3) for every 100 cm increase in water depth, increased 9 \nEUD/ha (85% CI = 6 – 12) for every 100 cm increase in Secchi depth, and increased 3 EUD/ha (85% CI = 0 – 11) if emergent vegetation was present.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".