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Sea Ice, Seals, and Settlement

2019· book-chapter· en· W3005536356 on OpenAlexaboutno aff
Christopher B. Wolff, Donald H. Holly, Victor D. Thompson

Bibliographic record

VenueUniversity Press of Florida eBooks · 2019
Typebook-chapter
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsSubsistence agricultureSubarctic climateGeographyLivelihoodIndigenousMarine conservationSettlement (finance)Subsistence economyHuman settlementEnvironmental changeMesolithicArchaeologyFisheryEcologyClimate changeBiology

Abstract

fetched live from OpenAlex

The cultural history of Newfoundland and Labrador is linked with the sea. The European occupation of this subarctic region was dependent on the abundance of Atlantic cod and other marine resources, such as seals, walrus, and whales. Precontact indigenous hunter gatherers of the region also relied heavily on marine ecology for their livelihood; yet, at various times in the region’s significant history, dynamic environmental and social conditions acted to change subsistence economies, cultures, and the course of its occupation. In this chapter, we examine archaeological, historical, and paleoecological evidence to assess the relative roles that environmental and social processes played in these critical transformations.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.160
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.033
GPT teacher head0.274
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 designQualitative
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
Published2019
Admission routes1
Has abstractyes

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