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Record W1945822327 · doi:10.1002/0470868333.ch14

Use of Tracers in Fluvial Geomorphology

2003· other· en· W1945822327 on OpenAlexaff
Marwan A. Hassan, Peter Ergenzinger

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFluvialArchaeologyGeographyGeologyGeomorphologyStructural basin

Abstract

fetched live from OpenAlex

This chapter contains sections titled: Introduction General Overview of Tracer Techniques Exotic Particles Painted Particles Fluorescent Paint Radioactive Tracers Ferruginous Tracers Magnetic Tracers Active Tracers: Radio Transmitters Suspended Load and Washload Tracing Case Study: Arroyo De Los Frijoles, New Mexico, USA (Leopold et al. 1966) Case Study: North Loup River, Nebraska, USA (Hubbell and Sayre 1964, Sayre and Hubbell 1965) Case Study: Nahal Hebron, Negev, Israel (Schick et al. 1987, Hassan et al. 1991, Hassan and Church 1992, 1994) Case Study: Lainbach, Germany (Ergenzinger et al. 1989, Schmidt and Ergenzinger 1992, Busskamp 1994, Gintz et al. 1996) Concluding Remarks Acknowledgements References

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.275
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2760.000

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 head0.016
GPT teacher head0.215
Teacher spread0.199 · 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
GenreOther

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

Citations80
Published2003
Admission routes1
Has abstractyes

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