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Record W4307923842 · doi:10.1134/s1028334x22700192

Reconstruction of Late Glacial Conditions of Exogenic Landscape Formation of Central Kamchatka: Data on Spore–Pollen Analysis

2022· article· en· W4307923842 on OpenAlexaff
E.O. Mukhametshina, Е. А. Зеленин, I. Florin Pendea

Bibliographic record

VenueDoklady Earth Sciences · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsLakehead University
Fundersnot available
KeywordsGlacial periodSubaerialGeologyHolocenePhysical geographyPollenGlacierClimate changeVegetation (pathology)PleistocenePaleontologyOceanographyGeographyEcology

Abstract

fetched live from OpenAlex

Spore–pollen analysis of lacustrine and subaerial sediments of the KamPlen reference section in the Central Kamchatka Depression (CKD) is conducted. The results of the analysis allowed the reconstruction of the CKD landscape formation conditions in the Late Peni-Glacial, Late Glacial, and the transition to the Holocene, which significantly expands the paleogeographical record elaborated for the Holocene of Kamchatka into the past. It is established that, after 18 ka (under relatively cold climate), the watershed of the paleolake that filled the CKD during the last glaciation was characterized by open landscapes with dominant herbaceous–grass communities. The presence of pollen of trees and warm water plants indicates the limited scales of mountainous–valley glaciation. The identified cooling period of 15–13 ka characterized by scarcer vegetation did not lead to a significant expansion of glaciers. After 13 ka, warming of the climate with a gradual degradation of glaciers resulted in regeneration of coniferous forests on the paleolake watershed. The drainage of the lake at ~11.5 ka BP and the beginning of sedimentation of subaerial deposits in the area of the studied section approximately correspond to the lower boundary of the Holocene, which confirms the key role of the climate at stages of the CKD landscape formation during the period considered.

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 score0.039
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.039
GPT teacher head0.272
Teacher spread0.232 · 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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