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Record W1444278871 · doi:10.1144/jgs2015-030

Decoupling seasonal fluctuations in fluvial discharge from the tidal signature in ancient deltaic deposits: an example from the Neuquén Basin, Argentina

2015· article· en· W1444278871 on OpenAlexaff
Marcello Gugliotta, Colleen Kurcinka, Robert W. Dalrymple, Stephen S. Flint, David M. Hodgson

Bibliographic record

VenueJournal of the Geological Society · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsQueen's University
Fundersnot available
KeywordsFluvialGeologyStructural basinDecoupling (probability)PaleontologySignature (topology)Geomorphology

Abstract

fetched live from OpenAlex

Fluvial discharge fluctuations are a fundamental characteristic of almost all modern rivers and can produce distinctive deposits that are rarely described from ancient fluvial or mixed-energy successions. Large-scale outcrops from the Middle Jurassic Lajas Formation (Argentina) expose a well-constrained stratigraphic succession of marginal-marine deposits with a strong fluvial influence and well-known tidal indicators. The studied deposits show decimetre-scale interbedding of coarser- and finer-grained facies with mixed fluvial and tidal affinities. The alternation of these two types of beds forms non-cyclic successions that are interpreted to be the result of seasonal variation in river discharge, rather than regular and predictable changes in current velocity caused by tides. Seasonal bedding is present in bar deposits that form within or at the mouth of minor and major channels. Seasonal bedding is not preserved in channel thalweg deposits, where river flood processes were too powerful, or in floodplain, muddy interdistributary-bay, prodelta and transgressive deposits, where the river signal was weak and sporadic. The identification of sedimentary facies characteristic of seasonal river discharge variations is important for accurate interpretation of ancient deltaic process regime.

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 score0.008
Threshold uncertainty score0.733

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.233
Teacher spread0.195 · 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

Citations57
Published2015
Admission routes1
Has abstractyes

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