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Record W4290723004 · doi:10.1007/s11004-022-10007-z

Application of Supersplining to the Mesozoic and Paleozoic Geologic Time Scales

2022· article· en· W4290723004 on OpenAlexaff
Felix M. Gradstein, Frits Agterberg

Bibliographic record

VenueMathematical Geosciences · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsGeological Survey of Canada
FundersUniversitetet i Oslo
KeywordsPaleozoicGeologyMesozoicOrdovicianCretaceousPaleontologyStage (stratigraphy)HydrogeologyPeriod (music)SmoothingGeologic time scaleBoundary (topology)StatisticsGeotechnical engineeringMathematicsStructural basin

Abstract

fetched live from OpenAlex

Abstract Methods to determine the ages of period and stage boundaries of the geologic time scale (GTS) have a long history and continue to be steadily improved. Stage boundary age estimates are now accompanied by error bars showing stratigraphic uncertainty. Most GTS2004, GTS2012 and GTS2020 results involved cubic spline-curve fitting, and in this study, Ordovician through Cretaceous smoothing splines of GTS2020 are spliced together to construct a Paleozoic–Mesozoic superspline. This methodology, its advantages and its results are outlined.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.011
GPT teacher head0.204
Teacher spread0.193 · 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.

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

Citations6
Published2022
Admission routes1
Has abstractyes

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