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Record W1492687136 · doi:10.22621/cfn.v120i1.249

Lake Sturgeon, <em>Acipenser fulvescens</em>, Movements in Rainy Lake, Minnesota and Ontario

2006· article· en· W1492687136 on OpenAlexfundvenueaboutno aff
Wells E. Adams, Larry W. Kallemeyn, David W. Willis

Bibliographic record

VenueThe Canadian Field-Naturalist · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersNational Park ServiceSouth Dakota Agricultural Experiment StationU.S. Geological SurveyMinistry of Natural ResourcesMinnesota Department of Natural Resources
KeywordsLake sturgeonAcipenserFisherySturgeonGeographyPopulationKettle (birds)Hydrology (agriculture)Fish <Actinopterygii>EcologyGeologyBiology

Abstract

fetched live from OpenAlex

Rainy Lake, Minnesota-Ontario, contains a native population of Lake Sturgeon (Acipenser fulvescens) that has gone largely unstudied. The objective of this descriptive study was to summarize generalized Lake Sturgeon movement patterns through the use of biotelemetry. Telemetry data reinforced the high utilization of the Squirrel Falls geographic location by Lake Sturgeon, with 37% of the re-locations occurring in that area. Other spring aggregations occurred in areas associated with Kettle Falls, the Pipestone River, and the Rat River, which could indicate spawning activity. Movement of Lake Sturgeon between the Seine River and the South Arm of Rainy Lake indicates the likelihood of one integrated population on the east end of the South Arm. The lack of re-locations in the Seine River during the months of September and October may have been due to Lake Sturgeon moving into deeper water areas of the Seine River and out of the range of radio telemetry gear or simply moving back into the South Arm. Due to the movements between Minnesota and Ontario, coordination of management efforts among provincial, state, and federal agencies will be important.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.993

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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.

Study designNot applicable
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

Citations12
Published2006
Admission routes3
Has abstractyes

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