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Record W2103255961 · doi:10.1139/z05-028

Distribution and density of glochidia of the freshwater mussel<i>Anodonta kennerlyi</i>on fish hosts in lakes of the temperate rain forest of Vancouver Island

2005· article· en· W2103255961 on OpenAlexvenueaboutno aff
André L. Martel, Jean‐Sébastien Lauzon‐Guay

Bibliographic record

VenueCanadian Journal of Zoology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyMusselSalvelinusUnionidaeEcologyFontinalisSculpinFisheryCottusBivalviaTroutMolluscaHabitatFish <Actinopterygii>

Abstract

fetched live from OpenAlex

We examined the distribution and abundance of glochidia of the freshwater mussel Anodonta kennerlyi Lea, 1860 on local fishes in three temperate rain forest lakes near Bamfield, on the west coast of Vancouver Island, British Columbia. Fishes involved in the life cycle of the mussel were the prickly sculpin (Cottus asper Richardson, 1836), threespine stickleback (Gasterosteus aculeatus L., 1758), Dolly Varden (Salvelinus malma (Walbaum, 1792)), and cutthroat trout (Oncorhynchus clarkii (Richardson, 1836)). For each lake, we assessed which fish was the most important for larval propagation and recruitment of the mussel by considering the fish's primary habitat, the percentage of fish in a sample with glochidia, and the abundance of glochidia on sampled fish. Also, an alternative method for quantifying the glochidia's "preference" for a host consisted of measuring the number of glochidia per unit area of fish body surface (larval density). We digitized the surface area of fins and head, i.e., the areas used by glochidia for settlement. Every fish species in each lake dispersed the glochidia. There was, however, a sharp gradient in the intensity of the fish–mussel linkage among fishes. Fishes that co-occurred most often with mussels, such as sculpins and sticklebacks, had the highest density of glochidia. Larval density on fishes also revealed the existence of between-lake differences in glochidia preference.

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 score0.950
Threshold uncertainty score1.000

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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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

Citations33
Published2005
Admission routes2
Has abstractyes

Explore more

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