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Record W3045228633 · doi:10.1086/710082

River management alters ecosystem metabolism in a large oligotrophic river

2020· article· en· W3045228633 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFreshwater Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceEcosystemHydrology (agriculture)Primary productionFloodplainHabitatEcosystem respirationFood webDischargeEcologyStreamflowContext (archaeology)Drainage basinGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Algae and aquatic plants support river food webs through in-situ primary production. However, gross primary production (GPP) and ecosystem respiration (ER) are rarely evaluated in the context of river management or habitat restoration. We estimated daily GPP and ER during 2 growing seasons for 7 reaches in the Kootenai River and 1 reach in the Elk River, spanning 290 river km across British Columbia, Canada, and Montana and Idaho, USA. We characterized responses of GPP and ER to river management, including reaches with unregulated flow, regulated flow, nutrient addition, and habitat restoration. Downstream GPP and ER generally increased after changes in river management, and higher management intensity led to greater increases. GPP and ER followed a seasonal pattern with low initial values in spring, elevated values in mid-summer, and a return to low values in late summer and autumn. Timing and duration of the elevated period for GPP and ER also differed among reaches following changes in river management. Our results suggest that river management affects GPP and ER, likely through reducing turbidity and the frequency and magnitude of extreme flow events, nutrient additions, and enhanced floodplain connectivity, thereby altering the timing and amount of autochthonous carbon available to the food web.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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