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Record W2762285853 · doi:10.1080/20555563.2017.1383086

Paleoindian Landscapes in Southeastern and Central New York

2017· article· en· W2762285853 on OpenAlexaboutno aff
Jonathan C. Lothrop, Michael L. Beardsley, Mark L. Clymer, Joseph E. Diamond, Philip C. LaPorta, Meredith H. Younge, Susan Winchell-Sweeney

Bibliographic record

VenuePaleoAmerica · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsnot available
Fundersnot available
KeywordsArchaeologyPleistoceneChronologyBedrockHoloceneLithic technologyGeographyProjectile pointStone toolGeologyPaleontology

Abstract

fetched live from OpenAlex

In 1957 and 1969, William A. Ritchie published data on geographic distributions of Paleoindian sites and points in the New York region. Discrete clusters of fluted bifaces and Paleoindian sites were apparent, variously associated with proglacial lake plains, bedrock lithic sources and other late Pleistocene landscapes. Since 2009, as part of the New York Paleoindian Database Project (NYPID), New York State Museum (NYSM) researchers and colleagues have been working with individuals and institutions to augment these early data sets on Paleoindian points and sites across the state. Our current research, focused on southeastern and central New York, substantiates the Paleoindian point/site clusters recorded by Ritchie in these two areas. Documenting settlement during the late Pleistocene and early Holocene, these point/site clusters are associated with former proglacial lake footprints in the Wallkill Valley and the Ontario Lowlands, respectively. These expanding data sets on the distribution, chronology, and lithic raw materials of these Paleoindian point and site clusters shed new light on the distinctive histories of early human occupation in these two sub-regions of New York.

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 score0.153
Threshold uncertainty score0.861

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 studies0.0000.000
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 head0.028
GPT teacher head0.288
Teacher spread0.259 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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