MétaCan
Menu
Back to cohort
Record W2582246213 · doi:10.11575/prism/25619

Developing a Systematic Sampling Framework for Terrestrial Gastropods in the Canadian Arctic

2016· dissertation· en· W2582246213 on OpenAlexaboutno aff
Joshua Sullivan

Bibliographic record

VenuePRISM (University of Calgary) · 2016
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicInvertebrate Taxonomy and Ecology
Canadian institutionsnot available
FundersStrong
KeywordsArcticSampling (signal processing)The arcticEnvironmental scienceOceanographyGeographyComputer scienceGeologyTelecommunications

Abstract

fetched live from OpenAlex

Two protostrongylid parasites of Arctic ungulates, Umingmakstrongylus pallikuukensis and Varestrongylus eleguneniensis, were recently discovered in muskoxen on Victoria Island, Nunavut. The subsequent range expansion and increasing prevalence of these lungworms on the island suggested that the temperature-dependent rate of larval development in the gastropod intermediate host was no longer constraining their range to the Arctic mainland. Thus, to determine if the ecology of the gastropod intermediate host would facilitate or restrict the further expansion and establishment of these parasites, a better understanding of the distribution, diversity and abundance of terrestrial gastropods on Victoria Island was needed. However, a description of the efficacy of gastropod sampling techniques on the tundra was lacking. Therefore, my research describes the first strategic sampling framework for assessing gastropod ecology in the Arctic. Additionally, I analyzed the influence of extrinsic factors on gastropod capture rates and described new geographical records for the intermediate host, Deroceras laeve. Keywords: Arctic, Gastropod, Deroceras laeve, Intermediate Host, Protostrongylidae

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)0.022
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.130
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0080.006
Science and technology studies0.0050.003
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.001
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 head0.225
Teacher spread0.190 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venuePRISM (University of Calgary)Same topicInvertebrate Taxonomy and EcologyFrench-language works237,207