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Record W2326133506 · doi:10.1017/s0032247412000368

Geoarchaeological importance of sub-Arctic bird perches dated by lee side turf mound accumulation and identified by lichen plumes

2012· article· en· W2326133506 on OpenAlexaff
P. J. Julig, William C. Mahaney, Volli Kalm, Judith R. Earl-Goulet

Bibliographic record

VenuePolar Record · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsLaurentian University
Fundersnot available
KeywordsArcticContext (archaeology)GeologyPeatNatural (archaeology)Subarctic climateThe arcticLoessArchaeologyEagleLichenGeoarchaeologyPhysical geographyEcologyPaleontologyGeographyOceanographyBiology

Abstract

fetched live from OpenAlex

ABSTRACT Organic-rich turf mounds, fertilised with bird droppings, may develop in the lee of obstacles, including either natural or human-made structures, boulders and rock cairns in exposed mountain and sub-Arctic and Arctic periglacial/proglacial environments. These boulder-leeside deposits consist mainly of organic materials intermixed with loess trapped in baffling agents (turf), and may sometimes contain archaeological features. Certain animal and bird species may also contribute to turf-mounds by frequenting these locations. Boulders and other natural and human-made obstructions are observed to have lichen plumes, principally Xanthoria spp? , lacing the tops and downwind side of the mound and fertilised by defecating birds and mammals. Turf mounds, consisting of organic soil, can be used for both relative and chronometric age dating, the buried peat providing 14 C dates for archaeological context for specific mountain sites when artefacts are recovered. In addition, field and laboratory analyses of soils provide important palaeoenvironmental and geoarchaeological information about the sites. Examples are provided from a field site in the Norra Storfjället in the Swedish SubArctic.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.285
Teacher spread0.240 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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