MétaCan
Menu
Back to cohort
Record W6894255135 · doi:10.5443/12044

Cliff edge, Tuktoyaktuk 1947 to 2008, Beaufort Sea, northern Canada

2016· dataset· en· W6894255135 on OpenAlexaboutno aff

Bibliographic record

VenueCanadian Polar Data Network · 2016
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAerial photographyCliffAerial surveyGeoreferenceBeaufort seaPhotogrammetryAerial photosPhotography

Abstract

fetched live from OpenAlex

The following dataset represents cliffs, beach and coastlines as they existed along the Beaufort Sea since 1947. These features provide an accurate representation of the land/water interface at a given time and position along the coastline. Aerial photography provided by National Airphoto Library (NAPL) and satellite imagery (Worldview 2 and Quickbird) provided the base layer for the dataset. The 2001 field survey data were used as the control for georeferencing the imagery. Digital coastlines, cliff lines and beach crests were generated from the accurately georeferenced aerial photography. The cliff depicted in the aerial photography was digitized as a line and saved as a vector file for each year that the base data was available. For more information please refer to the datafiles entitled: CCIN12044_20150114_Tuktoyaktuk_cliff_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)0.000
metaresearch head score (Gemma)0.004
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 score0.023
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.020
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.017

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 head0.018
GPT teacher head0.236
Teacher spread0.218 · 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

Explore more

Same venueCanadian Polar Data NetworkFrench-language works237,207