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Record W2017775023 · doi:10.1002/hyp.6789

An integrated framework of lake‐stream connectivity for a semi‐arid, subarctic environment

2007· article· en· W2017775023 on OpenAlexaffabout
Ming‐ko Woo, Corrinne Mielko

Bibliographic record

VenueHydrological Processes · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHydrology (agriculture)Subarctic climateSnowmeltSurface runoffEnvironmental scienceAridStreamflowWater balanceOutflowDrainage basinGeologyEcologyGeographyOceanography

Abstract

fetched live from OpenAlex

Abstract Lake‐stream networks dominate the semi‐arid, subarctic Precambrian shield. Such a network consists of a number of lake basin elements linked to each other by surface channels. To investigate the processes causing seasonal severance of flow connection in the lake‐stream system, a chain of lakes in northern Canada was studied in 2004. Water balance shows that rapid and substantial runoff from the local basin slopes during the snowmelt period led to a rise of lake levels above their outlet elevations to generate outflow. Continued summer evaporation caused draw down of lake storage below the outflow thresholds, represented by the lake outlet elevations. Outflow ceased and the lakes became disconnected. Summer rainfall in a semi‐arid environment was insufficient to overcome storage deficit to re‐establish flow connectivity among all lakes. For the drainage system as a whole, streamflow interruption or continuity depends on the linkage of its lake‐stream sub‐units. The principle of fill and spill governs runoff generation and flow connection between the lake elements. This principle is applied to a conceptual model of flow along a chain of lakes, taking account of (1) antecedent storage in individual lakes, (2) their storage change calculated through water balance and (3) the thresholds to be exceeded for outflows to occur. Copyright © 2007 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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.015
GPT teacher head0.252
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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

Citations36
Published2007
Admission routes2
Has abstractyes

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