Bibliographic record
Abstract
Ralph C. Keef was born in St. Stephens, New Brunswick, Canada, in December 1933. He grew up in Vanceboro, Maine. His father and grandfather owned land and woods operations in Washington County and in Charlotte County, New Brunswick. Keef attended the University of Maine where he received a B.S. in Chemical Engineering, a Certificate in Pulp and Paper Management, and in 1958, an M.S. in Pulp and Paper Technology.\nKeef’s professional career began in Ketchikan and Sitka, Alaska with the Alaska Lumber and Pulp Company. In 1961, Keef joined the St. Croix Paper Company in Woodland, Maine and then, as a result of a merger was employed by Georgia Pacific. In 1967, Keef joined Great Northern Paper in Millinocket.\nIn 1973, Keef became the Resident Manager and Chairman of the Executive Committee of Nova Scotia Forest Industries in Port Hawkesbury, a mill complex owned by the Swedish-owned company Stora Kopparberg. He later transferred to their paper mill in New Falls, New York as Vice President where he stayed until retiring in 1987.\nKeef recipient served as a member of the University of Maine’s Pulp & Paper Foundation Investment Management Committee becoming its Chair in 2001. Keef died on July 9, 2015, at his home in Hermon, Maine following a series of strokes.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.114 | 0.050 |
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".