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Record W4327795828 · doi:10.4095/331539

Canada west coast topo-bathymetric digital elevation model, British Columbia

2023· report· en· W4327795828 on OpenAlexaffabout
R Kung, K Douglas, Cooper Stacey

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsBathymetryElevation (ballistics)Digital elevation modelGeospatial analysisGeographyOceanographyRemote sensingCartographyGeologyEngineering

Abstract

fetched live from OpenAlex

The joint Natural Resources Canada/Department of Fisheries and Oceans Marine Spatial Planning Program requires the highest resolution bathymetric elevation data and adjacent land-based topographic elevation data that are available. This digital elevation model of Canada's west coast compiles the best data available from multiple government agencies to create a regional model gridded at 10 metre spacing. The transitions between the marine and terrestrial areas are near-seamless creating a surface of elevations for non-navigational scientific research, cartographic, and general marine spatial planning use. The product is available on the Federal Geospatial Platform at: https://open.canada.ca/data/en/dataset/e6e11b99-f0cc-44f7-f5eb-3b995fb1637e

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)0.003
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 score0.050
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.017
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0500.026

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 head0.024
GPT teacher head0.219
Teacher spread0.195 · 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

Citations16
Published2023
Admission routes2
Has abstractyes

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