Does every goose count? Pitfalls of surveying breeding geese in urban areas
Bibliographic record
Abstract
The size of local breeding populations of Greylag Geese Anser anser and Canada Geese Branta canadensis at a suburban site in Northrhine-Westphalia, Germany, was assessed between 2010 and 2012 using four different methods: nest surveys, counts of territorial pairs and two types of brood counts.For both species, nest surveys generated the highest estimate of breeding numbers.Geese recorded as territorial pairs made up 50-75% of the apparent nesting pairs (73% of all nesting Greylag Geese and 60% of all nesting Canada Geese in an area surveyed extensively in 2011).Numbers of broods recorded never exceeded 50% of the number of apparent nesting pairs.Moreover, the number of broods observed was heavily dependent on fieldwork intensity, with most broods found during highly frequent (twice-weekly) counts that allowed effective monitoring of the fate of individual broods, even without using individual marking.When broods are monitored less frequently, one has to rely on the maximum number of broods observed simultaneously in determining the number of pairs with young, which in our study represented only 10-25% of the apparent nesting number.Although nest counts may provide the highest estimate of breeding goose abundance, they may be impractical or undesirable (e.g. because of disturbance to other breeding birds).In such cases, territorial pair assessments may be the preferred method, if separation of breeding and non-breeding birds is not made too conservatively.For instance, only those birds that obviously behave as non-breeders, by leaving the nesting areas to feed on nearby agricultural fields during daytime, should be excluded from breeding numbers.Although counts of the total number of broods can contribute to measures of reproductive success, they can considerably underestimate the number of goose breeding pairs.Key words: breeding bird census,
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".