Canada Goose Weed Dispersal and Nutrient Loading in Turfgrass Systems
Bibliographic record
Abstract
High populations of Canada geese (Branta canadensis L.) can lead to feces accumulation in areas adjacent to surface waters, creating concern about aquatic eutrophication. Further, turf managers and livestock farmers work to keep their facilities free of noxious or toxic weeds that geese potentially disperse. We investigated the prevalence of viable seeds and nitrogen and phosphorus content in resident Canada goose droppings. During spring, summer, and fall of 2008, we collected 127 fresh individual droppings which were placed in seedling trays within an irrigated greenhouse and allowed 30 days for weed seed to germinate. Trays were cold stratified for 30 days and returned to the greenhouse for an additional 30 days. Also, during summer and fall of 2007 and 2008, we tested 304 fecal samples from 8 sites for total Kjeldahl nitrogen (TKN) and total phosphorus (TP). Out of 127 droppings planted, 4 plants germinated (3.1%): Pennsylvania smartweed (Polygonum pennsylvanicum L.), annual bluegrass (Poa annua L.), and 2 Kyllinga spp. The average amounts of TKN and TP in fecal samples were 24.2 mg/g (range = 12.6 to 55.7) and 3.6 mg/g (range = 1.4 to 8.3) of dry matter, respectively. The results indicate that Canada geese in suburban and urban areas are not frequent vectors of viable seeds, but do have potential to contribute nutrients to adjacent surface waters.
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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 | 0.001 |
| 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".