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Record W2132788415 · doi:10.1002/hyp.10527

Comparison of threshold hydrologic response across northern catchments

2015· article· en· W2132788415 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

VenueHydrological Processes · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsMcMaster UniversityGlobal Institute for Water SecurityTrent UniversityUniversity of SaskatchewanUniversity of Manitoba
FundersUniversity of AberdeenLeverhulme Trust
KeywordsSnowmeltEnvironmental scienceSurface runoffMeltwaterPrecipitationWatershedSnowHydrology (agriculture)Drainage basinClimate changeMeteorologyGeologyGeographyEcology

Abstract

fetched live from OpenAlex

Abstract Nine mid‐latitude to high‐latitude headwater catchments – part of the Northern Watershed Ecosystem Response to Climate Change (North‐Watch) programme – were used to analyze threshold response to rainfall and snowmelt‐driven events and link the different responses to the catchment characteristics of the nine sites. The North‐Watch data include daily time‐series of various lengths of multiple variables such as air temperature, precipitation and discharge. Rainfall and meltwater inputs were differentiated using a degree‐day snowmelt approach. Distinct hydrological events were identified, and precipitation‐runoff response curves were visually assessed. Results showed that eight of nine catchments showed runoff initiation thresholds and effective precipitation input thresholds. For rainfall‐triggered events, catchment hydroclimatic and physical characteristics (e.g. mean annual air temperature, median flow path distance to the stream, median sub‐catchment area) were strong predictors of threshold strength. For snowmelt‐driven events, however, thresholds and the factors controlling precipitation‐runoff response were difficult to identify. The variability in catchments responses to snowmelt was not fully explained by runoff initiation thresholds and input magnitude thresholds. The quantification of input intensity thresholds (e.g. snow melting and permafrost thawing rates) is likely required for an adequate characterization of nonlinear spring runoff generation in such northern environments. Copyright © 2014 John Wiley & Sons, Ltd.

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)0.001
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 score0.008
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.112
GPT teacher head0.334
Teacher spread0.222 · 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