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Record W6912860376 · doi:10.5443/11408

Canada's Three Oceans (C3O)

2012· dataset· en· W6912860376 on OpenAlexaboutno aff

Bibliographic record

VenueCanadian Polar Data Network · 2012
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCoast guardArcticThe arcticPacific oceanPlanktonSeawater

Abstract

fetched live from OpenAlex

The Canada¿s Three Ocean¿s project is designed to take a snapshot of all three of the oceans surrounding Canada in 2007 and 2008. In doing this, we will evaluate the connections among the Arctic, Pacific and Atlantic Oceans, provide a baseline of biological, chemical and physical measurements of the ocean environment and leave a legacy that can be used as the basis for long term monitoring of our oceans. In 2008, following the success of the 2007 season, two Canadian Coast Guard icebreakers left their home ports on the Atlantic and Pacific coasts carrying scientists who observed the environment around them by measuring a wide range of properties, from the numbers and type of seabirds, to the plankton in the water, the nutrients in the seawater to the physical and chemical properties of seawater that tell the story of the water¿s current movement and past history. By the time they crossed paths in the Canadian Arctic Archipelago, 14000km of ocean from Victoria to Halifax through the Northwest Passage had been observed in one season. Researchers from government and universities, students at all levels from high school and up, writers, photographers, and technicians both experienced and in training participated in this project. Once home from sea they have been analysing, processing and plotting their results which together with the results from 2007 give two single season snapshots of Canada¿s surrounding oceans.

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)0.004
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 score0.036
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.017
Science and technology studies0.0030.001
Scholarly communication0.0040.001
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.013

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 head0.034
GPT teacher head0.237
Teacher spread0.203 · 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
Published2012
Admission routes1
Has abstractyes

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Same venueCanadian Polar Data NetworkFrench-language works237,207