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Record W1922112072 · doi:10.1002/2014wr016839

Whither field hydrology? The need for discovery science and outrageous hydrological hypotheses

2015· article· en· W1922112072 on OpenAlexaff
T. P. Burt, Jeffrey J. McDonnell

Bibliographic record

VenueWater Resources Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
Fundersnot available
KeywordsWatershedField (mathematics)Formative assessmentSurface runoffWork (physics)Process (computing)Scientific discoveryHydrology (agriculture)Environmental resource managementEnvironmental ethicsComputer scienceEnvironmental scienceSociologyEcologyGeologyMathematicsPsychologyEngineeringCognitive scienceBiology

Abstract

fetched live from OpenAlex

Abstract Field hydrology is on the decline. Meanwhile, the need for new field‐derived insight into the age, origin and pathway of water in the headwaters, where most runoff is generated, is more needed than ever. Water Resources Research (WRR) has included some of the most influential papers in field‐based runoff process understanding, particularly in the formative years when the knowledge base was developing rapidly. Here we take advantage of this 50th anniversary of the journal to highlight a few of these important field‐based papers and show how field scientists have posed strong and sometimes outrageous hypotheses—approaches so needed in an era of largely model‐only research. We chronicle the decline in field work and note that it is not only the quantity of field work that is diminishing but its character is changing too: from discovery science to data collection for model parameterization. While the latter is a necessary activity, the loss of the former is a major concern if we are to advance the science of watershed hydrology. We outline a vision for field research to seek new fundamental understanding, new mechanistic explanations of how watershed systems work, particularly outside the regions of traditional focus.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.072
GPT teacher head0.311
Teacher spread0.240 · 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 designNot applicable
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

Citations189
Published2015
Admission routes1
Has abstractyes

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