MétaCan
Menu
Back to cohort
Record W2619506828 · doi:10.1002/2017gl072686

Evaluating the size and extent of paleolakes in central Tibet during the late Pleistocene

2017· article· en· W2619506828 on OpenAlexaff
Xuhua Shi, Kevin P. Furlong, Eric Kirby, Kai Meng, Shasta M. Marrero, John Gosse, Erchie Wang, Fred M. Phillips

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsDalhousie University
FundersNanyang Technological UniversityNational Research Foundation SingaporeNational Research FoundationChinese Academy of SciencesEarth Observatory of SingaporeNational Science Foundation
KeywordsShoreGeologyPleistoceneInterglacialChronologyPeriod (music)Marine isotope stageElevation (ballistics)QuaternaryOceanographyPhysical geographyPaleontologyClimatologyGeography

Abstract

fetched live from OpenAlex

Abstract Subhorizontal lake shorelines allow a geodynamic test of the size and extent of a hypothesized paleolake in central Tibet, the East Qiangtang Lake (EQL), during the last interglacial period (marine isotope stage (MIS) 5e). Reconstructions based on relict lake deposits suggest that the EQL would have been ~400 m deep and over ~66,000 km2. Models of flexural rebound driven by lake recession predict that shorelines near the EQL center, at the present‐day location of Siling Co, would have rebounded 60–90 m above their initial elevation. New36Cl chronology of the highest relict shorelines around Siling Co indicates that they reflect lake levels between 110 and 190 ka. These shorelines, however, are presently >300 m below their predicted elevations, implying a substantially smaller water load. Our results reveal that the expansion of Tibetan lakes during MIS 5e was relatively limited. Instead, individual lakes were supplied by river networks, much as they are today.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.066
GPT teacher head0.352
Teacher spread0.286 · 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

Citations28
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicGeology and Paleoclimatology ResearchFrench-language works237,207