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Record W2147111500 · doi:10.1134/s0016702906020017

Endogenic cycles and the problem of crustal growth

2006· article· en· W2147111500 on OpenAlexaboutno aff
Yu. A. Balashov, V. N. Glaznev

Bibliographic record

VenueGeochemistry International · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsnot available
FundersRussian Foundation for Basic Research
KeywordsGeologyEarth scienceGeochemistryAstrobiologyPetrologyPaleontologyBiology

Abstract

fetched live from OpenAlex

The statistical analysis of geochronological data (more than 14200 dates) has been carried out by using the method of the probabilistic description of summary information on the crust and mantle and separately on both of the Earth’s upper shells considered together over the whole geological history. Various lines of evidence are presented for the necessity of using the whole set of geochronological methods to reveal any systematic pattern in the evolution of crust formation and to demonstrate the uselessness of utilizing selected data obtained by any one of the methods because of the limited analytical capability of each of them. These constraints, together with compositional variations of the dated rocks and the variable amount of the initial information, lead to uncertainty in estimation of megacyclicity as a sum of contrasting dynamics of endogenic events occurring in the crust and the mantle. It has been shown that mantle processes become more intense during periods of the synchronous activation of endogenic events in both shells; mantle activity sharply decreases in the epochs when endogenic processes in these shells are waning. This difference may serve as an objective criterion for estimating the maximum duration of cycles of mantle activity, which is distinct in the Early-Middle Archean, Late Archean-Proterozoic, and Phanerozoic. These conclusions are supported by examples of geochronological systematics for cratons of northeastern Labrador, western Greenland, and western Australia.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.174
Teacher spread0.170 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

Explore more

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