MétaCan
Menu
Back to cohort

Sedimentary Patterns and Spatial Distributions of Heavy Minerals along the Continental Shelf in the Espírito Santo State, Brazil

2022· article· en· W4302283587 on OpenAlexaff
Adeildo De Assis Costa Júnior, Valéria da Silva Quaresma, Caio Vinícius Gabrig Turbay Rangel, Natacha Oliveira, Marcos Daniel de Almeida Leite, Fernanda V. Vieira, Alex Cardoso Bastos

Bibliographic record

VenueAnuário do Instituto de Geociências · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsContinental (Canada)
FundersFundação de Amparo à Pesquisa e Inovação do Espírito Santo
KeywordsSedimentary rockContinental shelfGeologyHeavy mineralSedimentationSedimentGeochemistryMaturity (psychological)OceanographyGeomorphology

Abstract

fetched live from OpenAlex

Heavy minerals can be used as tools to better understand sedimentary patterns across continental shelves, in addition to their economic importance, where they form marine placers. This study investigates the spatial distributions of heavy minerals in sand deposits along the three different morpho-sedimentary compartments (i.e., Paleovalley Shelf, Doce river Shelf, and Abrolhos Shelf) of the Espírito Santo Continental Shelf, which presents distinct sedimentary regimes. A mineralogical characterization of 180 surface sediment samples allowed to identify fifteen different heavy mineral species across the study area, with a predominance of ilmenite. The qualitative characterization shows similar heavy mineral patterns among the three compartments, while their mineral proportion (quantitative analysis) is heterogeneous. Also, the supply and accommodation regimes do not responsible by influence the heavy mineral assemblages and sediment maturity. However, there is a significant relationship between supply regime (delta sedimentation) and higher average abundances of each heavy mineral species. With results found here is possible to affirm that marine placers are closely related to Holocene sedimentation.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.228
Teacher spread0.217 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueAnuário do Instituto de GeociênciasSame topicPaleontology and Stratigraphy of FossilsFrench-language works237,207