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Record W2739607583 · doi:10.1093/biosci/bix078

Terrestrial Invertebrates in the Riparian Zone: Mechanisms Underlying Their Unique Diversity

2017· article· en· W2739607583 on OpenAlexfundno aff
Tonya L. Ramey, John S. Richardson

Bibliographic record

VenueBioScience · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsRiparian zoneEcologyInvertebrateEnvironmental scienceGeographyRiparian forestBiodiversityEcosystemThreatened speciesBiologyHabitat

Abstract

fetched live from OpenAlex

Riparian areas are biologically rich ecosystems, but they are highly threatened across the globe. Invertebrates represent a large proportion of the animal diversity within riparian areas, perform various ecological functions, and serve as bioenergetic links between aquatic and riparian food webs. Although many studies have been done on riparian taxa, this is the first synthesis of invertebrate ecology in riparian areas. We first discuss five characteristics of riparian zones that may support specialist riparian invertebrates: high rates of disturbance, elevated nutrient and water availability, increased vegetation and microhabitat diversity, strong microclimate gradients, and unique food resources. We then review how these mechanisms change with distance from the stream (lateral gradients) and position within a catchment (longitudinal gradients) and the resulting effects on the riparian community. Finally, we highlight gaps in our current knowledge and offer suggestions for future research.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.062
GPT teacher head0.247
Teacher spread0.185 · 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

Citations104
Published2017
Admission routes1
Has abstractyes

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