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Record W2074559481 · doi:10.1645/ge-2011.1

Parasites of the Deepwater Sculpin (Myoxocephalus thompsonii) Across Its Canadian Range

2009· article· en· W2074559481 on OpenAlexaffabout
Joseph P. Carney, Tom A. Sheldon, Nathan R. Lovejoy

Bibliographic record

VenueJournal of Parasitology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsUniversity of TorontoRoyal Military College of CanadaLakehead University
Fundersnot available
KeywordsBiologySculpinCestodaHelminthsParasite hostingAcanthocephalaSalvelinusEcologyRange (aeronautics)ZoologyTroutTrophic levelFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Deepwater sculpin (Myoxocephalus thompsonii) were collected from 19 lakes across the species' distribution in Canada and examined for parasites. Six helminth species (Crepidostomum farionis, Bothriocephalus cuspidatus, Proteocephalus sp., Cyathocephalus truncatus, Raphidascaris acus, and Echinorhynchus salmonis), 1 crustacean species (Ergasilus nerkae), and 1 molluscan species (glochidia) parasitized these hosts. Crepidostomum farionis, Proteocephalus sp., R. acus, E. nerkae, and the glochidia represent new parasite records for this host species. Overall parasite prevalence was 78.0% while mean intensity was 6.1 +/- 7.1 SD. Bothriocephalus cuspidatus was the most prevalent parasite and was recorded from 62.2% of the deepwater sculpin and found in 17 of the 19 lakes. The low-productivity habitat of this host limits the parasites available for transmission, and the infra- and component communities were generally species poor. With the exception of the Proteocephalus sp., all of the helminth parasites recovered have been reported as adults in lake trout (Salvelinus namaycush) or burbot (Lota lota), suggesting that, in the lakes where they occur, deepwater sculpin may play an important role in energetic transfer and parasitic transmission to higher trophic levels.

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 score0.024
Threshold uncertainty score0.991

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.355
Teacher spread0.342 · 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

Citations3
Published2009
Admission routes2
Has abstractyes

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