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Record W2792462953 · doi:10.1139/cjes-2017-0170

Palynomorphs from a lacustrine sequence provide evidence for palaeoenvironmental changes during the early Miocene in Central Anatolia, Turkey

2018· article· en· W2792462953 on OpenAlexvenueno aff
Demet Bi̇lteki̇n

Bibliographic record

VenueCanadian Journal of Earth Sciences · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Diversity and Evolution
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyVegetation (pathology)DeciduousMediterranean climatePaleontologyLate MioceneRange (aeronautics)Physical geographyGeographyEcologyStructural basinArchaeology

Abstract

fetched live from OpenAlex

Pollen samples belonging to 54 plant taxa from 32 intervals in a 59 m thick lacustrine sequence in the Harami open pit lignite mine in Konya Province, Turkey, were studied to evaluate early Miocene (Aquitanian) changes in vegetation, climate, and environments in Central Anatolia. The regional vegetation consisted of forests, whose composition changed through time. An abundance of sub-tropical and warm-temperate, mainly deciduous trees in the lower part of Harami section indicates a warm and humid climate in the region during the earliest Miocene. A trend towards cooler and drier conditions, driven by a combination of regional and local factors, is reflected by the sequential establishment of two kinds of coniferous forests: a cedar (Cedrus) dominated forest in the middle part of the section, followed by a mixed cedar and pine (Pinus) forest in the upper part of the section. The high percentages of Cedrus in the middle and upper portions of the Harami section suggest that high-elevation coniferous forests were growing around mountain ranges. Although cedars have been a prominent part of the Anatolian flora since at least the early Miocene, in historical times their geographical range throughout the Mediterranean has been dramatically reduced by human activities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.962

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.222
Teacher spread0.156 · 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.

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

Citations11
Published2018
Admission routes1
Has abstractyes

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