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Record W6969649729 · doi:10.5443/12025

Cliff top, Clarence Lagoon 1976-1996, Ivvavik National Park, Yukon, northern Canada

2016· dataset· en· W6969649729 on OpenAlexaboutno aff

Bibliographic record

VenueCanadian Polar Data Network · 2016
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsShoreCoastal erosionCliffBeaufort seaAerial photographyArcticSatellite imageryNational park

Abstract

fetched live from OpenAlex

Field work and airphoto studies were undertaken between 1953-1996 along the Yukon coast of the Beaufort Sea in Ivvavik National Park to determine coastal erosion rates, processes and hazards in relation to archeological and cultural heritage sites, many of which are concentrated along the coast in this region. The work was carried out on behalf of Parks Canada (Canadian Heritage, Western Arctic District, Inuvik) at Niakolik Point, Stokes Point, Catton Point/Ptarmigan Bay, Nunaluk Spit and Clarence Lagoon. Shoreline positions digitized from aerial photography (NAPL) and more recent satellite imagery to calculate coastline change rates at key representative sites in the region. This approach provides a general indication of how the coast has behaved under the range of climatic and oceanographic conditions which prevailed during the time intervals encompassed by the air photographs. For more information please refer to the datafiles entitled: CCIN12025_20150122_Clarence_Lagoon_cliff_top_YEAR_Extended_Metadata_FGDC.xml

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.047
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.026
Science and technology studies0.0020.000
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0470.021

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 head0.029
GPT teacher head0.250
Teacher spread0.221 · 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 designNot applicable
Domainnot available
GenreDataset

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 venueCanadian Polar Data NetworkFrench-language works237,207