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Record W2140250974 · doi:10.1073/pnas.0307570100

Prehistoric Inuit whalers affected Arctic freshwater ecosystems

2004· article· en· W2140250974 on OpenAlexafffundabout
Marianne S. V. Douglas, John P. Smol, James M. Savelle, Jules M. Blais

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2004
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMcGill UniversityQueen's UniversityUniversity of TorontoUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsArcticLimnologyCircumpolar starBiotaPrehistoryEcologyEcosystemGeographyPopulationPaleolimnologyFreshwater ecosystemHuman settlementAquatic ecosystemArchaeologyOceanographyHoloceneBiologyGeology

Abstract

fetched live from OpenAlex

It is commonly assumed that High Arctic lakes and ponds were not affected by direct local human activities before the arrival of Europeans, because most native peoples were primarily nomadic, maintained relatively low population densities, and practiced unintrusive hunting and gathering technologies. Our archeological and paleolimnological data show that this was not always the case. Thule Inuit whalers, whose winter settlements consisted of houses constructed from the bones of bowhead whales on Somerset Island between about anno Domini 1200 and 1600, markedly changed pond water quality and ecology. The arrival of whalers 8 centuries ago caused marked changes in water chemistry and the expansion of moss substrates. Although whalers abandoned the area >4 centuries ago, the legacy of these human disturbances is still evident in the pond's present-day limnology and is characterized by elevated nutrient concentrations and atypical biota. This is the earliest reported paleolimnological record of changes in aquatic ecology associated with local human activities in Canada or the United States, or for any circumpolar ecosystem.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.055
GPT teacher head0.355
Teacher spread0.300 · 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

Citations104
Published2004
Admission routes3
Has abstractyes

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