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Record W4387327684 · doi:10.1080/00288330.2023.2264226

A preliminary investigation of zooplankton diapausing eggs from waterbird faecal droppings in New Zealand

2023· article· en· W4387327684 on OpenAlexaboutno aff
Kelly M. Jamieson, Ian C. Duggan

Bibliographic record

VenueNew Zealand Journal of Marine and Freshwater Research · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsZooplanktonBiological dispersalBiologyEcologyAbundance (ecology)FisheryCharadriiformesPopulation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.301
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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