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Record W2022643534 · doi:10.1139/z99-196

The importance of microhabitat factors and habitat stability to the threatened Louisiana pearl shell, <i>Margaritifera hembeli</i> (Conrad)

2000· article· en· W2022643534 on OpenAlexvenueno aff
Paul D. Johnson, Kenneth M. Brown

Bibliographic record

VenueCanadian Journal of Zoology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
FundersU.S. Forest Service
KeywordsMusselMargaritiferaCobbleThreatened speciesSubstrate (aquarium)HabitatEcologyEndangered speciesRiffleSTREAMSTributaryBiologyFisheryGeography

Abstract

fetched live from OpenAlex

Margaritifera hembeli, the Louisiana pearl shell, is a threatened mussel with a distribution limited to the headwaters of three tributaries of the Red River in central Louisiana, U.S.A. We assessed the role that several habitat characters played in determining its abundance and distribution. Pearl shell mussels were more common in second-order streams with elevated conductivity (approximately 0.04 mS/cm) and water hardness (8 mg/L). A discriminant analysis indicated that mussel density was related to water depth, substrate size, substrate compaction, and water velocity. Mussels were rare in deep, stagnant pools with silt-covered bottoms, and were more common in shallow, wide areas of streams with higher current velocities and in sediments with larger particle sizes. Mussel beds were also more likely to occur in sections of the stream where the substrate was more stable through time. These habitat associations may occur because individuals that recruit into, or later select, more stable microhabitats, have an advantage owing to the relatively long life cycle of this mussel. We suggest that the measurement of microhabitat characteristics can be important when evaluating habitat preferences and management plans for endangered mussel species in headwater streams.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.994

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)0.0060.000

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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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 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

Citations68
Published2000
Admission routes1
Has abstractyes

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