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Record W2902761781

«ЛЮДИ СЕВЕРА» В ПЛЕЙСТОЦЕНЕ: ПАЛЕОЛИТИЧЕСКИЕ ВЕХИ И ПЕРЕХОДНЫЕ ГОРИЗОНТЫ В СЕВЕРНОЙ ЕВРАЗИИ ЧАСТЬ I: РАННЕПАЛЕОЛИТИЧЕСКИЕ ПРЕДКИ

2016· article· ru· W2902761781 on OpenAlexaff
Н. Роллан

Bibliographic record

VenueArchaeology Ethnology and Anthropology of Eurasia (Russian-language) · 2016
Typearticle
Languageru
FieldEarth and Planetary Sciences
TopicMarine and environmental studies
Canadian institutionsResearch Canada
Fundersnot available
KeywordsMiddle PaleolithicMousterianUpper PaleolithicBeringiaHomo erectusGeographyPleistoceneHuman evolutionPaleontologyHominidaeSteppeBiogeographyPerspectivismBiomeArchaeologyCaveEcologyGeologyBiological evolutionBiology
DOInot available

Abstract

fetched live from OpenAlex

Human occupation of northern Eurasia high latitudes entailed coping with severe bioclimatic circumstances and Ice Age cycle fl uctuations. Resolving this “adaptability paradox” required depending on cultural, rather than biological means. Paleolithic evidence indicates culture historical developments of considerable time depth, long-term adaptive stages and thresholds in the “peopling of the North”. It began with Lower Paleolithic populations expanding into temperate and continental Eurasia, becoming fully actualized during the Middle and Upper Paleolithic. The Middle Paleolithic Formative Stage constituted a human biogeographic realm overlapping signifi cantly with the Mammoth-Steppe-Biome faunal complex. Part I identifi es issues, “time perspectivism”, culture, foraging adaptation, and human biogeography concepts. Lower Paleolithic occurrences, initial occupation episodes are surveyed and discussed.

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)0.002
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueArchaeology Ethnology and Anthropology of Eurasia (Russian-language)Same topicMarine and environmental studiesFrench-language works237,207