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Record W6931708367 · doi:10.5683/sp3/1wgbtg

Lac Lacoursière Quebec. 1:50,000. Map Sheet 022N08, ed. 1, 1967

2022· dataset· en· W6931708367 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2022
Typedataset
Languageen
FieldComputer Science
TopicData Analysis with R
Canadian institutionsnot available
Fundersnot available
KeywordsGeoreferenceGeneral partnershipRaster graphicsNatural (archaeology)Aerial photographyGeographic information systemDigital mappingViewshed analysis

Abstract

fetched live from OpenAlex

This is a georeferenced raster image of a printed paper map of the Lac Lacoursière, Quebec region (Sheet No. 022N08), published in 1967. It is the first edition in a series of maps, which show both natural and man-made features such as relief, spot heights, administrative boundaries, secondary and side roads, railways, trails, wooded areas, waterways including lakes, rivers, streams and rapids, bridges, buildings, mills, power lines, terrain, and land formations. This map was published in 1967 and the information on the map is current as of 1959. Maps were produced by Natural Resources Canada (NRCan) and it's preceding agencies, in partnership with other government agencies. <b>Please note:</b> image / survey capture dates can span several years, and some details may have been updated later than others. Please consult individual map sheets for detailed production information, which can be found in the bottom left hand corner. Original maps were digitally scanned by McGill Libraries in partnership with Canadiana.org, and georeferencing for the maps was provided by the University of Toronto Libraries and Eastview Corporation.

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 score0.170
Threshold uncertainty score0.569

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.021
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1700.101

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 head0.011
GPT teacher head0.251
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 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
Published2022
Admission routes1
Has abstractyes

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