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Record W2754462520 · doi:10.1139/cjz-2017-0078

Leeches (Annelida: Hirudinea) from the far north of Ontario: distribution, diversity, and diagnostics

2017· article· en· W2754462520 on OpenAlexafffundvenueabout
Sarah V. Langer, Kathryn A. Vezsenyi, Danielle de Carle, David Beresford, Sebastian Kvist

Bibliographic record

VenueCanadian Journal of Zoology · 2017
Typearticle
Languageen
FieldMedicine
TopicLeech Biology and Applications
Canadian institutionsRoyal Ontario MuseumUniversity of TorontoTrent University
FundersNatural Sciences and Engineering Research Council of CanadaUniverza v LjubljaniOntario Ministry of Natural Resources and ForestryMinistry of Natural Resources
KeywordsBiologyLeechHabitatEcologyDistribution (mathematics)Sampling biasWetlandDNA barcodingSampling (signal processing)Identification (biology)GeographySample size determination

Abstract

fetched live from OpenAlex

Leeches have a worldwide distribution, yet numerous geographical regions remain to be adequately surveyed. Here, we present leech species records for one of these regions: the far north of Ontario, Canada. This region is primarily wetland habitat and includes two of Ontario’s three ecozones. Morphological examinations, as well as a single instance of successful DNA amplification with subsequent molecular identification, allowed us to identify representatives of 12 species from two predatory families (Erpobdellidae and Haemopidae) and one parasitic family (Glossiphoniidae) among samples of 130 individuals. To provide a more inclusive list of species records for this remote region, our data were also augmented by 25 largely unpublished collection records (for 102 individuals) from the Canadian Museum of Nature, which revealed the presence of an additional species. We comment on finds of particular interest in our sampling with comparison to relevant literature and provide new distribution data for these species.

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)0.000
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 score0.199
Threshold uncertainty score0.401

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.236
Teacher spread0.206 · 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

Citations11
Published2017
Admission routes4
Has abstractyes

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