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Record W1558156211 · doi:10.1017/cbo9780511975622.013

Trace fossils in sequence stratigraphy

2011· book-chapter· en· W1558156211 on OpenAlexaff
Luís A. Buatois, M. Gabriela Mángano

Bibliographic record

VenueCambridge University Press eBooks · 2011
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTRACE (psycholinguistics)Sequence (biology)PaleontologyNatural historyMetaphorCharles darwinSequence stratigraphyDarwin (ADL)HistoryArchaeologyGeologyGenealogyBiologyEvolutionary biologyPhilosophyComputer scienceEcologyDarwinismLinguistics

Abstract

fetched live from OpenAlex

For my part, following out Lyell’s metaphor, I look at the natural geological record, as a history of a world imperfectly kept, and written in a changing dialect; of this history we possess the last volume alone, relating only to two or three countries. Of this volume, only here and there a short chapter has been preserved; and of each page, only here and there a few lines. Charles Darwin On the Origin of Species (1859) Trace fossils are proving to be one of the most important groups of fossils in delineating stratigraphically important boundaries related to sequence stratigraphy. George Pemberton and James MacEachern “The sequence stratigraphic significance of trace fossils: examples from the Cretaceous Foreland Basin of Alberta, Canada” (1995) The appearance of sequence stratigraphy in the late eighties resulted in a revolution in the study of sedimentary rocks. The shift from seismic stratigraphy (Vail et al ., 1977) to sequence stratigraphy brought the incorporation of outcrops and cores as sources of data in stratigraphic analysis (Posamentier et al ., 1988; Posamentier and Vail, 1988; Van Wagoner et al ., 1990). Coincident with this shift, ichnological studies began to emphasize the importance of trace fossils in sequence stratigraphy (e.g. Savrda, 1991b; MacEachern et al ., 1992; Pemberton et al ., 1992b). In little more than a decade, the field experienced a rapid increase in the number of studies devoted to exploring the applicability of ichnology in refining sequence-stratigraphic analysis (e.g. MacEachern et al ., 1992, 1999a, 2007c; Savrda et al ., 1993; Taylor and Gawthorpe, 1993; Pemberton and MacEachern, 1995; Ghibaudo et al ., 1996; Martin and Pollard, 1996; Buatois et al ., 1998d, 2002b; Pemberton et al ., 2001, 2004; Carmona et al ., 2006). At present, ichnological aspects are currently covered in sequence-stratigraphic textbooks (e.g. Catuneanu, 2006). The aim of this chapter is to provide a detailed review of the applications of ichnology in sequence stratigraphy. Although a large part of this chapter deals with the recognition of discontinuity surfaces in marine siliciclastic successions, we will also cover other topics which are commonly overlooked in the literature. These include characterization of parasequences, parasequence sets, and systems tracts, but also the potential of trace fossils to address sequence-stratigraphic issues in carbonates and continental deposits.

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)0.001
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.007
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.005

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 head0.056
GPT teacher head0.204
Teacher spread0.147 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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