A preliminary investigation of zooplankton diapausing eggs from waterbird faecal droppings in New Zealand
Bibliographic record
Abstract
ABSTRACT We analysed internal dispersal of zooplankton by waterbirds (endozoochory) in New Zealand, quantifying zooplankton eggs in faecal droppings collected at two lakes, Lake Rotoroa (Hamilton) and Lake Rotorua. Sixty‐seven faecal droppings were collected from Mallard Ducks (20), Canada Geese (11), Greylag Geese (6), Black Swans (20) and Australian Coots (10). Fifty eggs were found, with a mean of 0.75 eggs per dropping, indicating that waterbirds consume zooplankton eggs, and that these pass through the digestive system. No significant difference was observed in the abundance of eggs among waterbird species, and no eggs hatched in the laboratory. Our results suggest that waterbird dispersal of zooplankton in New Zealand is occurring, but numbers being transported are low. Further, as non‐native waterbirds such as mallard ducks and geese do not migrate in New Zealand to the extent they do elsewhere, they are likely not primary vectors for zooplankton dispersal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".