An efficient method for capturing flightless geese
Bibliographic record
Abstract
Populations of local breeding or resident Canada geese (Branta canadensis) are widespread and are increasing in the United States (Sheaffer et al. 1987). As these populations have grown, so has the interest in these birds for aesthetic, recreational, and research purposes. This growth also has led to an increase in conflicts with human populations (Nelson and Oetting 1981, Conover and Chasko 1985). Examples of conflicts include damage or nuisance problems on lawns, golf courses, public beaches, parks, and agricultural fields. Other concerns include human health and safety issues such as aircraft collision risk and increased nutrient loading of ponds and public water supplies. Resident geese often are captured and relocated to resolve nuisance problems (Woronecki et al. 1990) or trapped for marking purposes during research or management studies. Trapping is often conducted during the bird's annual prebasic molt in mid-summer. Geese lose all their flight feathers simultaneously during this molt and are flightless for a period of several weeks. During this period, they can be rounded up and caught relatively easily. The normal capture method involves erecting a corral or catch pen using chicken-wire or nylon net fencing. The catch pen is held in place by threading support poles through the wire or net and hammering them into the ground. Lines or guide wires are often used to stabilize the pen, and ground boards may be needed to secure the bottom of the net to prevent birds from escaping underneath. Fence lines are erected in a similar manner at an angle leading out from the pen and help direct or funnel the geese. Geese are walked or herded toward the funnel and into the catch pen. This catch pen, although relatively simple, can take 30-60 minutes to erect and another 20-30 minutes to dismantle. In addition, the area where birds can be caught is limited. The trapping location must be relatively level so that birds cannot pass under the fence or pen and the ground must be soft enough to drive in the support poles. The process can be equipment heavy, time consuming, and inefficient, especially if only a small number of geese are to be captured. We describe a more efficient capture technique using lightweight, portable panels to herd and surround geese into a moveable catch pen. This method is quick, requires little equipment and preparation, and can be done almost anywhere, including hard surfaces such as pavements. This capture method also has been used to catch flightless (nuisance) ducks and mute swans. This report was completed in part with funds provided under the Federal Aid to Wildlife Restoration Program.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".