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Habitat shifts and parasite loads of lesser snow geese (Chen caerulescens caerulescens)

2006· article· en· W2177718140 on OpenAlexvenueno aff
Asha Mellor, Robert F. Rockwell

Bibliographic record

VenueEcoscience · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatEcologySnowMarshBiologyWaterfowlGoosePopulationBaySalt marshAnatidaeWetlandGeography

Abstract

fetched live from OpenAlex

Responding to degradation in their original coastal habitat, increasing numbers of lesser snow geese are rearing their broods farther inland. Goslings collected in this inland, fresh water habitat have substantially lower loads of two species of caecal nematodes than do goslings collected in coastal, salt marsh habitat. This likely reflects differences between the habitats in the levels of infective stages of the parasites that are ingested by goslings during their summer foraging. In the spring, several million northward migrating adult lesser snow geese use the coast of Hudson Bay for staging and feeding rather than using more inland habitat because the latter is usually still snow- and icebound. The spring migrants leave behind copious amounts of feces in the coastal marshes that contain the eggs and larvae of the nematodes. By contrast, the inland habitat receives little fecal deposition until mid-summer and then only by the much smaller resident population of nesting lesser snow geese. There is some evidence that the infectious stages of these parasites survive the winter, but multi-year accumulations would only tend to amplify habitat differences in infective loads related to the spring deposition by migrants. The role of migrants in transmitting these nematodes highlights the important point that local host–parasite dynamics must be considered from a broader spatial scale.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.066
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.275
Teacher spread0.267 · 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 teacher head, 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

Citations7
Published2006
Admission routes1
Has abstractyes

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