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Record W2076855704 · doi:10.1577/h04-002.1

Thiamine Content and Thiaminase Activity of Ten Freshwater Stocks and One Marine Stock of Alewives

2005· article· en· W2076855704 on OpenAlexaffabout
John D. Fitzsimons, Bill Williston, James L. Zajicek, Donald E. Tillitt, Scott Brown, Lisa R. Brown, Dale C. Honeyfield, David M. Warner, Lars G. Rudstam, Webster Pearsall

Bibliographic record

VenueJournal of Aquatic Animal Health · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsEnvironment and Climate Change CanadaFisheries and Oceans Canada
FundersU.S. Geological SurveyGreat Lakes Fishery Trust
KeywordsAlewifeThiamineBiologyEcologyFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract Alewives Alosa pseudoharengus contain thiaminase activity that has been implicated in the development of a thiamine deficiency and associated effects in salmonines of the Great Lakes basin. Little is known about the factors that regulate thiaminase activity in alewives. We sampled alewives of uniform size (60–120 mm) during the summer of 1998 from the Gulf of St. Lawrence, seven of New York's Finger Lakes, one inland lake in Ontario, and two Great Lakes to assess possible relationships among thiamine, lipid content, fish abundance, lake morphometry, lake productivity, freshwater residency, and thiaminase activity. Thiaminase activity varied significantly among the 11 locations but was unrelated to thiamine concentration, which did not vary significantly. Alewife thiaminase activity in the Finger Lakes was negatively related to lipid content and positively related to measures of lake size (e.g., area, volume, and maximum depth). Activity in the one marine stock sampled in the Gulf of St. Lawrence was comparable to the highest values observed in the 10 freshwater stocks examined. Variation in alewife thiaminase activity has the potential to affect the extent of a thiamine deficiency associated with salmonines who feed on alewives as well as the viability of their offspring.

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)0.001
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 score0.046
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.032
GPT teacher head0.262
Teacher spread0.230 · 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

Citations56
Published2005
Admission routes2
Has abstractyes

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