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Record W3070430439 · doi:10.1002/esp.4980

Field tests of an improved sediment tracer including non‐intrusive measurement of burial depth

2020· article· en· W3070430439 on OpenAlexafffund
Aryn Cain, Bruce MacVicar

Bibliographic record

VenueEarth Surface Processes and Landforms · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsDillon ConsultingUniversity of Waterloo
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsGeologyTRACERSedimentSediment transportChannel (broadcasting)Hydrology (agriculture)GeomorphologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Radio‐frequency identification (RFID) transponders are now widely used to track sediment in a variety of environments. A recent innovation placed the transponder inside of a rotating inner mechanism that is designed to minimize missed detections due to burial and shielding or ‘signal collision’ effects between tracers, while also allowing a rapid measurement of the burial depth of the particle. Here we test a developed protocol for burial depth measurement and deploy the ‘Wobblestone’ tracers in the field for the first time. Results show that new tracers can be reliably positioned in the horizontal plane (median error ± 0.03 m) and that the burial depth can be accurately measured (~0.02 m maximum error). The field study was characterized by high mobility and travel lengths, and ~20% of the tracers were buried at depths up to 0.15 m. A comparison of exponential distributions for travel length of surface deposited and buried tracers indicate that the buried tracers on average traveled farther and earlier in the flood event. Tracers that did not move were also buried at one site as a result of sediment transport from upstream. Overall the technique has great potential for characterizing vertical mixing and understanding this rarely considered control on sediment transport. © 2020 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.529

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)0.0000.000

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 head0.019
GPT teacher head0.235
Teacher spread0.216 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2020
Admission routes2
Has abstractyes

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