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2018· article· en· W4230873954 on OpenAlexaff
Paul F. Hoffman

Bibliographic record

VenueAnnual Review of Earth and Planetary Sciences · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSnowball EarthProterozoicRodiniaGeologyPaleontologyPlate tectonicsGlacierGlacial periodGeologic recordAstrobiologyEarth scienceTectonicsCraton

Abstract

fetched live from OpenAlex

The Proterozoic Eon was once regarded as the neglected middle half of Earth history. The name refers to early animals, but they did not appear until the eon (2.5–0.54 Ga) was nearly over. Eukaryotic cells and sexual reproduction evolved much earlier in the eon, as did chloroplasts. Molecular dioxygen, the presence of which altered the geochemical behavior of nearly every element essential to life, rose from negligible to near-modern levels, and then plummeted before rising fitfully again. Plate tectonics took on a modern form, and two supercontinents, Nuna and Rodinia, successively congregated and later dispersed. Climate regulatory failures, i.e., Snowball Earth, appear to be a uniquely Proterozoic phenomenon, having occurred twice in rapid succession near the end of the eon (from 717 to 660 Ma and from 650 to 635 Ma) and arguably once near its beginning (ca. 2.43 Ga). Dynamic sea glaciers covered Snowball Earth oceans from pole to pole, and equatorial sublimation drove slow-moving ice sheets on land. Ultimately, the gradual accumulation of CO 2 triggered rapid deglaciation and transient greenhouse aftermaths. Physically based and geologically tested, Neoproterozoic Snowball Earth appears to have molecular legacies in ancient bitumens and modern organisms. This is the story of my love affair with an eon that is now a little less neglected.

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)0.004
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 score0.177
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0070.009
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.1770.066

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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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