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Record W4311820083 · doi:10.1093/biosci/biac098

Causes, Responses, and Implications of Anthropogenic versus Natural Flow Intermittence in River Networks

2022· article· en· W4311820083 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioScience · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMcGill University
FundersNational Science Foundation of Sri LankaEuropean CommissionNational Science Foundation
KeywordsBiotaBiodiversityEcosystemEphemeral keyEnvironmental scienceNatural (archaeology)EcologyGeographyBiology

Abstract

fetched live from OpenAlex

Abstract Rivers that do not flow year-round are the predominant type of running waters on Earth. Despite a burgeoning literature on natural flow intermittence (NFI), knowledge about the hydrological causes and ecological effects of human-induced, anthropogenic flow intermittence (AFI) remains limited. NFI and AFI could generate contrasting hydrological and biological responses in rivers because of distinct underlying causes of drying and evolutionary adaptations of their biota. We first review the causes of AFI and show how different anthropogenic drivers alter the timing, frequency and duration of drying, compared with NFI. Second, we evaluate the possible differences in biodiversity responses, ecological functions, and ecosystem services between NFI and AFI. Last, we outline knowledge gaps and management needs related to AFI. Because of the distinct hydrologic characteristics and ecological impacts of AFI, ignoring the distinction between NFI and AFI could undermine management of intermittent rivers and ephemeral streams and exacerbate risks to the ecosystems and societies downstream.

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.

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)0.000
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 score0.010
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.001
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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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