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Record W4214543457 · doi:10.1111/ter.12588

Introduction to the special issue “Tibetan tectonics and its effect on the long‐term evolution of climate, vegetation and environment”

2022· article· en· W4214543457 on OpenAlexaff
Yuntao Tian, Guangsheng Zhuang, Junsheng Nie, Qiang Xu, Yaowu Xing, Andrew V. Zuza, Joel E. Saylor, Ryan J. Leary, Alexander Rohrmann

Bibliographic record

VenueTerra Nova · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of British Columbia
FundersNational Science Foundation of Sri LankaNational Natural Science Foundation of China
KeywordsPlateau (mathematics)TectonicsEarth scienceClimate changeBiodiversityGeologyRange (aeronautics)Earth system sciencePhysical geographyGeographyClimatologyPaleontologyOceanographyEcology

Abstract

fetched live from OpenAlex

Abstract The long‐term evolution of the Tibetan Plateau significantly influenced Asian climate, nearby ocean physics and chemistry, and terrestrial biodiversity. This range of impacts has attracted research attention from a correspondingly broad range of disciplines, providing important new insights into prolonged and emerging debates concerning the Himalayan–Tibetan morphotectonic evolution and its impacts on the long‐term evolution of climate, biodiversity, and the environment on regional to global scales. To communicate the latest advances on this coupled tectonic, climatic and biological system, we have launched a special virtual issue in Terra Nova and solicited submissions of 20 papers in total. The papers cover a wide range of topics that fall in the following, partially overlapping, categories: pre‐India–Asia collision tectonic configuration; post‐collision deformation; sedimentary system: source to sink studies, climatic forcing, and river incision; and climatic and biospheric influence of the Tibetan Plateau.

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 score0.013
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.013
GPT teacher head0.232
Teacher spread0.220 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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