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Record W2418259542 · doi:10.5539/esr.v5n2p105

Fluvial Erosion Characterisation in the Juqueriquerê River Channel, Caraguatatuba, Brazil

2016· article· en· W2418259542 on OpenAlexvenueno aff
Victor F. Velázquez, Viviane Dias Alves Portela, José Maria Azevedo Sobrinho, Antonio Carlos Moretti Guedes, Mikhaela A. J. S. P Letsch

Bibliographic record

VenueEarth Science Research · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsnot available
FundersUniversidade de São PauloFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsFluvialGeologyBedrockErosionGeomorphologyChannel (broadcasting)PrecambrianMetamorphic rockTectonicsGeochemistryPaleontology

Abstract

fetched live from OpenAlex

The Juqueriquerê River channel was formed in a Precambrian crystalline basement. The lithological association is largely composed of ancient metamorphic and igneous rocks, with several overlapping tectonic episodes. Field surveys along the upper and middle course allowed for cataloguing a wide variety of fluvial erosion features. A sizable amount of morphological features have been sculpted on different types of rocks, including furrows, potholes, percussion marks, polishing and smoothing boulders as the most representative. The sizes and shapes of these scour marks are also diverse, and their study has provided important results for better understanding the erosive processes. Given their wide variety, the erosive morphological features offer an excellent opportunity to explore the mechanisms of fluvial erosion and evaluate their effective capacity to remove cobbles and boulders in bedrock river systems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.545

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.065
GPT teacher head0.332
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations2
Published2016
Admission routes1
Has abstractyes

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