A lift‐net method for capturing diving and sea ducks
Bibliographic record
Abstract
ABSTRACT Alternatives to bait‐trapping waterfowl are often necessary when studying health or body condition, when targeting species that are not easily attracted to bait or those that occur in deep‐water habitats where bait‐trapping can be difficult. We designed an active netting method for capturing diving and sea ducks, which we deployed at 8 sites in Lake Ontario, Canada from 2006 to 2007 and 2011 to 2012. A mist net suspended horizontally 0.5 m below the water surface was lifted out of the water when ducks swam over the capture area. The technique requires a stationary structure to anchor one end and is lifted out of the water by hand or using a vehicle on the non‐anchored end using attached ropes. We used structures including docks and walls of a shipping channel to secure ropes, but other structures could be used. Catch rates were 0.63 birds/hr for greater scaup ( Aythya marila ), and 0.65 birds/hr for long‐tailed ducks ( Clangula hyemalis ). In comparison, catch rates for floating mist nets were 0.08 birds/hr and 0.86 birds/hr for greater scaup and long‐tailed ducks, respectively. Equipment costs to build a lift‐net were 85% less than to build a floating mist net. © 2013 The Wildlife Society.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".