Yield Mapping to Document Goose Grazing Impacts on Winter Wheat
Bibliographic record
Abstract
In southwestern Washington and western Oregon, increasing numbers of wintering Canada geese ( Branta canadensis ) graze several farm crops including wheat ( Triticum aestivum L.). Our objectives were to develop methods to determine timing, intensity, and locations of grazing, and to measure the impact of grazing on grain yield. Aerial photography with ground‐truth photography and repeated sampling worked well to determine timing, intensity, and locations of grazing. Exclosures served as nongrazed controls. A yield‐mapping‐system‐equipped combine measured yields. All data collection points were spatially located via differential global positioning system (DGPS) technology, which allowed us to integrate all data spatially and temporally via geographical information system (GIS) technology. Based on yield‐mapping‐system data, goose grazing resulted in grain yield differences ranging from a 16% increase on part of one field to a 25% decrease on an area of a field heavily grazed in April, just before geese migrated north. Comparisons of exclosures (nongrazed controls) with paired plots provided variable results. Results from paired‐plot comparisons for three fields during 1998 were 25% reduction, no difference, and 13% increase in grain yields in the paired plots available for grazing vs. exclosures. Based on yield‐mapping‐system data, the same three fields experienced 19, 7, and 5% grain yield reductions due to goose grazing with the extent of reduction depending on a combination of timing, intensity, and extent of grazing. Paired plots did not adequately represent grazing impacts on the fields. The yield‐mapping system provided nearly complete coverage of the fields and adequately captured grazing impacts.
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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 | 0.000 |
| 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) | 0.004 | 0.004 |
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; both teacher heads agree on what is shown here.
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".