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Record W2790215300 · doi:10.1144/sp470.5

Supercontinents and the case for Pannotia

2018· article· en· W2790215300 on OpenAlexafffund
R. Damian Nance, J. Brendan Murphy

Bibliographic record

VenueGeological Society London Special Publications · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsSt. Francis Xavier University
FundersNatural Sciences and Engineering Research Council of CanadaCurtin University of Technology
KeywordsGeologyPaleontology

Abstract

fetched live from OpenAlex

Abstract Disagreement about the existence of the late Neoproterozoic supercontinent Pannotia highlights the limitation of defining supercontinents simply on the basis of size, which, for pre-Pangaean supercontinents, is difficult to determine. In the context of the supercontinent cycle, however, supercontinent assembly and break-up, respectively, mark the end of one cycle and the beginning of the next and can be recognized by the tectonic, climatic and biogeochemical trends that accompany them. Hence supercontinents need only be large enough to influence mantle circulation in such a way as to enable the cycle to repeat. Their recognition need not rely solely on continental reconstructions, but can also exploit a variety of secular trends that accompany their amalgamation and break-up. Although the palaeogeographical and age constraints for the existence of Pannotia remain equivocal, the proxy signals of supercontinent assembly and break-up in the late Neoproterozoic are unmistakable. These signals cannot be readily attributed to either the break-up of Rodinia or the assembly of Gondwana without ignoring either the assembly phase of Pan-African orogenesis and the changes in mantle circulation that accompany this phase, or the reality that Gondwana cannot be a supercontinent in the context of the supercontinent cycle because its break-up coincides with that of Pangaea.

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)0.000
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 score0.206
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

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 head0.019
GPT teacher head0.224
Teacher spread0.205 · 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

Citations65
Published2018
Admission routes2
Has abstractyes

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