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Seasonal meso‐ and microhabitat selection by the northern snakehead (<i>Channa argus</i>) in the Potomac river system

2010· article· en· W2137659511 on OpenAlexfundno aff
N. W. R. Lapointe, James T. Thorson, Paul L. Angermeier

Bibliographic record

VenueEcology Of Freshwater Fish · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Geological SurveyVirginia Department of Game and Inland Fisheries
KeywordsHabitatSnakeheadFisheryEcologyMacrophyteBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Lapointe NWR, Thorson JT, Angermeier PL. Seasonal meso‐ and microhabitat selection by the northern snakehead ( Channa argus ) in the Potomac river system. Ecology of Freshwater Fish 2010: 19: 566–577. © 2010 John Wiley &amp; Sons A/S Abstract – The northern snakehead ( Channa argus ) is a large piscivorous fish that is invasive in eastern Europe and has recently been introduced in North America. We examined the seasonal habitat selection at meso‐ and microhabitat scales using radio‐telemetry to increase understanding of the ecology of this species, which will help to inform management decisions. After the spawning season (postspawn season, September–November), northern snakeheads preferred offshore Eurasian water‐milfoil ( Myriophyllum spicatum ) beds with shallow water (∼115 cm) and soft substrate. In the winter (November–April), these fish moved to deeper water (∼135 cm) with warmer temperatures, but habitat selection was weak at both scales. Northern snakeheads returned to shallower water (∼95 cm) in the prespawn season (April–June) and used milfoil and other cover. Habitat selection was the strongest at both meso‐ and microhabitat scales during the spawning season (June–September), when fish preferred macrophytes and cover in shallow water (∼88 cm). Our results help to identify habitats at the risk of invasion by northern snakeheads. We suggest that control efforts and future research focus on shallow waters, and take into consideration the seasonal habitat preferences.

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 score0.371
Threshold uncertainty score0.641

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 studies0.0000.001
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 head0.004
GPT teacher head0.182
Teacher spread0.179 · 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

Citations22
Published2010
Admission routes1
Has abstractyes

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