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Record W3170789007 · doi:10.2110/jsr.2020.143

The Stratigraphy Machine

2021· article· en· W3170789007 on OpenAlexaff
Andrew D. Miall, John Holbrook, Janok P. Bhattacharya

Bibliographic record

VenueJournal of Sedimentary Research · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsStratigraphyGeologyPaleontologyMagnitude (astronomy)Range (aeronautics)SedimentationSection (typography)Stratigraphic sectionErosionSedimentSedimentary rockTectonicsComputer science

Abstract

fetched live from OpenAlex

ABSTRACT There is a significant difference between the average sedimentation rate of a lengthy stratigraphic section spanning many millions of years, and the rate that can be calculated from any short segment within such a section, such segments typically yielding rates several orders of magnitude more rapid than the overall rate. Stratigraphic successions contain numerous surfaces of nondeposition and erosion representing time spans from minutes to many millions of years, which collectively may account for as much as 90% of the total elapsed time that the succession represents. The stratigraphic record is constructed by a range of geological processes that operate over all time scales from seconds to billions of years, and at rates that vary by ten orders of magnitude. The generation of the stratigraphic record can be conceptualized in the form of a mechanical device, which we term the “Stratigraphy Machine.”

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0340.011

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 head0.062
GPT teacher head0.327
Teacher spread0.265 · 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 designNot applicable
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

Citations16
Published2021
Admission routes1
Has abstractyes

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