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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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.233
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.024
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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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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