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Record W2782861887 · doi:10.1002/2017wr020707

The T‐TEL Method for Assessing Water, Sediment, and Chemical Connectivity

2018· article· en· W2782861887 on OpenAlexaff
Geneviève Ali, Claire Oswald, Christopher Spence, Christopher Wellen

Bibliographic record

VenueWater Resources Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsEnvironment and Climate Change CanadaToronto Metropolitan UniversityUniversity of Manitoba
Fundersnot available
KeywordsEnvironmental scienceWatershedSurface runoffWater resourcesProcess (computing)SedimentResource (disambiguation)Computer scienceHydrology (agriculture)Environmental resource managementGeologyEcologyMachine learningGeomorphology

Abstract

fetched live from OpenAlex

Abstract The concept of connectivity has been the subject of a great deal of recent research and provided new insights and breakthroughs on runoff generation processes and watershed biogeochemistry. However, a consensus definition and cohesive mathematical framework that would permit the consistent quantification of hydrologic connectivity, the examination of the interrelationships between water and material (e.g., sediment and chemicals) connectivity, or rigorous study intercomparison, have not been presented by the water resource community. Building on previous conceptualizations and site‐specific or process‐specific metrics, this paper aimed to review the current state of science on hydrologic connectivity and its role in water‐mediated connectivity of material such as solutes and sediment before introducing a conceptual and a mathematical connectivity assessment framework. These frameworks rely on the quantification of Time scales, Thresholds, Excesses and Losses related to water and water‐mediated material transport dynamics and are referred to as the T‐TEL method. Through a small case study, we show how the T‐TEL method allows a wide range of properties to be quantified, namely the occurrence, frequency, duration, magnitude, and spatial extent of water and water‐mediated material connectivity. We also propose a research agenda to refine the T‐TEL method and ensure its usefulness for facilitating the research and management of connectivity in pristine and human‐impacted landscapes.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.047
GPT teacher head0.365
Teacher spread0.318 · 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 designBench or experimental
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

Citations41
Published2018
Admission routes1
Has abstractyes

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