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Record W2162616594 · doi:10.1139/x03-089

Communities of aquatic insects of old-growth and clearcut coastal headwater streams of varying flow persistence

2003· article· en· W2162616594 on OpenAlexvenueno aff
Karen Price, Arlene Suski, Joanna McGarvie, Barbara A. Beasley, John S. Richardson

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPerennial streamSTREAMSDetritusEcologyEphemeral keyEnvironmental scienceAbundance (ecology)Biomass (ecology)Perennial plantBiology

Abstract

fetched live from OpenAlex

Headwater streams, varying in flow persistence from ephemeral to intermittent to perennial, provide the tightest coupling between water and land, yet they often receive the least protection during forest management. We described communities of aquatic insects in perennial, intermittent, and ephemeral channels surrounded by old-growth forest and 4- to 8-year-old clearcuts in Clayoquot Sound, British Columbia, to determine whether temporary streams have unique aquatic communities and to examine the short-term impacts of harvesting. We measured flow persistence, stream size, canopy cover, organic detritus, and algal biomass in 19 streams. We sampled aquatic invertebrates with a combination of emergence cages and kicknet samples. Temporary and old-growth streams had more organic detritus and a higher abundance of shredders. Perennial and clearcut streams had a higher abundance of some algal grazers, but not higher algal biomass. Insect richness was similar in intermittent and perennial streams of each seral stage but lower in ephemeral streams. Intermittent streams contained four taxa not found in the other stream classes; perennial and ephemeral streams had none. Communities of aquatic insects differed between streams surrounded by clearcuts and old growth, and varied with continuity of flow.

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 score0.410
Threshold uncertainty score0.894

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.239
Teacher spread0.189 · 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

Citations49
Published2003
Admission routes1
Has abstractyes

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