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Record W7099814514

Benthic macroinvertebrate assemblages of coastal and continental streams and large rivers of southwestern British

2000· article· en· W7099814514 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsSTREAMSBenthic zoneSpecies richnessInvertebrateAbundance (ecology)Continental shelfCommunity structureBiodiversity
DOInot available

Abstract

fetched live from OpenAlex

In southwestern British Columbia (BC, Canada) and within a relatively small geographic area, lotic environments range from streams in coastal rainforests, to streams in arid continental grasslands, to very large rivers. Little is known about the invertebrate communities in large rivers in general, or in the streams of continental BC. The purpose of this study was to determine whether the benthic invertebrate community structure changes spatially between small coastal and small interior streams; between small streams versus large rivers; and whether changes in the benthic community are related to the environmental conditions. Kicknet samples and environmental data were collected from three coastal streams, three continental streams and two large rivers (discharge of 781 and 3620 m3/s, respectively). The large river sites had low invertebrate abundance, species richness and diversity, relative to the small streams. The coastal streams had the highest species richness and the continental streams had the highest invertebrate abundance. A number of taxa were specific to each class of stream. Invertebrate abundance decreased with river size, and increased with elevation, pH, conductivity, alkalinity, NO2NO3-N, total Kejldahl nitrogen and percent carbon in suspended solids.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.053
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.032
GPT teacher head0.238
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2000
Admission routes1
Has abstractyes

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