In Defense of Field Experiments: Response to Askham and Godfrey (2014)
Bibliographic record
Abstract
Two knowledgeable colleagues have taken exception to some research conducted by us on Canada geese (Branta canadensis) published in the last issue of Human–Wildlife Interactions (Dieter et al. 2014). We appreciate the opportunity to respond. Regarding bird feeding behavior, Askham and Godfrey were correct in their assertion that evaluation of bird behavior on agricultural crops is poorly understood. Birds will indeed sometimes feed on plants treated with a chemical repellent if they have no other choice. However, the authors cite unpublished data (by Askham) stating that 32 times the recommended amount of methyl anthranilate (MA) was needed to prevent birds from feeding after food deprivation (in a pen trial, we assume, since it was not stated). . . . The research project we reported on was conducted to determine if there was a chemical that works to deter crop damage by geese in field conditions in South Dakota. Because we found that anthraquinone showed some promise, we are now working on refining recommendations as to use of the chemical. We are currently examining application rates, timing of application, number of applications needed, and area of the field that needs treatment. We do not have any vested interest in specific chemical companies. In fact, we would prefer it if no additional chemicals were introduced into the environment. However, the application of a chemical that works well to deter crop damage would be welcomed by farmers, game managers, and sportsmen alike. The use of an effective chemical to deter crop damage by geese may be preferable to some of the current lethal management techniques being used.
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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 | 0.080 | 0.166 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.051 | 0.055 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".