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Record W6963725172 · doi:10.21963/11942

Gastropod surveys in south-eastern Victoria Island

2016· dataset· en· W6963725172 on OpenAlexaboutno aff

Bibliographic record

VenueCanadian Cryospheric Information Network · 2016
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatMarshWetlandRange (aeronautics)Freshwater molluscTerrestrial ecosystemAbundance (ecology)Gastropoda

Abstract

fetched live from OpenAlex

As the range of the muskox lungworms Umingmakstrongylus pallikuukensis and Varestrongylus nov. sp. on Victoria Island, Nunavut have recently expanded, a critical look at the distribution and abundance of their gastropod intermediate hosts is imperative for identifying regions susceptible to future parasite colonization. During July-September 2013, we sampled for gastropods at five terrestrial sites and five freshwater sites on south-eastern Victoria Island, near the hamlet of Cambridge Bay. At each terrestrial site four habitats were described and sampled: these habitats were fen meadows, shrub-sedge meadows, and moist upland and mesic habitats. Gastropods were surveyed systematically using two terrestrial techniques, a dampened mat technique and a turf flooding technique. At the freshwater sites we opportunistically sampled several lakes, rivers, streams and marshes using a dip-net. Terrestrial surveys were conducted over three nights and any gastropods present were collected daily. One species of terrestrial slug, Deroceras laeve, was found during the surveys. It was present in low densities across three of the habitats: fen meadows, shrub-sedge meadows and moist upland habitat. We did not find any freshwater gastropods or snail shells during the surveys. Data are available in both excel and csv formats.

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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.108
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.192
Teacher spread0.184 · 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
GenreDataset

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

Citations0
Published2016
Admission routes1
Has abstractyes

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