On site with Maurice Haycock, artist of the Arctic: painting and drawing of historical sites in the Canadain Arctic
Bibliographic record
Abstract
It’s not surprising that Maurice Haycock’s art project combines aspects of a geological survey with characteristics of the practice of the dominant Canadian landscape painters of the early 20th century. His father was a professor of geology at Acadia University in Wolfville, Nova Scotia, and Maurice chose to follow in his footsteps, graduating from Acadia in 1926, and then going on to Princeton University where he received a PhD in Economic Geology in 1931. He then joined the Bureau of Mines in the Canadian Department of Resources and Development in Ottawa, where he worked until his retirement in 1965. Young Haycock took summer jobs every year between 1918 and 1931 with the Geological Survey of Canada, which gave him invaluable field experience, and in 1926, as a break before undertaking graduate work, he accepted a 15-month assignment with the Survey in the eastern Arctic. Returning near the end of August 1927, he boarded the government supply ship The Beothic in Pangnirtung, which was that summer carrying the well-known “Group of Seven” artist A.Y. Jackson and his friend, the famous scientist and amateur painter, Dr Frederick Banting. It was a chance encounter that ultimately would bring a significant new dimension to Haycock’s life.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".