MétaCan
Menu
Back to cohort
Record W7027024226

Burnett, Bill oral history interview

2013· other· en· W7027024226 on OpenAlexaboutno aff

Bibliographic record

VenueSummit (Simon Fraser University) · 2013
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsOral historyPort (circuit theory)TicketWhite (mutation)West coastEast coastWork (physics)On board
DOInot available

Abstract

fetched live from OpenAlex

Bill Burnett is a retired Fraser River pilot, whose main duties were to ensure that ships were kept from running around. The official river pilot station he worked at was called Sand Hedge, a lighthouse at the mouth of the Fraser River. River pilots help navigate a ship to and from the dock from a point c. 3 miles beyond the lighthouse. At 8 years old, Burnett knew he wanted to be a pilot after meeting a Dungeness pilot at a family wedding. (Dungeness is a port near Dover, England.) Burnett received his pilot training in France and Poland. In 1969, while in the process of getting his masters ticket (certification as a captain), he became enamored with the idea of working on tugs in British Columbia. During this time he was working on a Shell Canada tanker (which never went to Canada) sailing from the east coast to Venezuela. Since Burnett was employed by Shell he had Canadian employment and immigrated to Canada. Once on the west coast he first worked for Northland Navigation delivering groceries, then Vancouver Tugs (which became Seaspan) until things “went sour” in 1971 and he was cut from his job.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.240
Threshold uncertainty score0.803

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0180.002
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.2400.044

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 head0.028
GPT teacher head0.210
Teacher spread0.182 · 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 designQualitative
Domainnot available
GenreEmpirical

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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueSummit (Simon Fraser University)French-language works237,207