Bibliographic record
Abstract
Alequiers is the story of a one–hundred–year–old log house on the banks of the Highwood River in Southern Alberta, with particular emphasis on the time that author Mike Schintz and his family spent there. The book details what little is known about Alexander McQueen Weir, the original settler on the site and goes on to describe the changes in structure that took place under succeeding occupants, the Royle and Schintz families. The book is also a tribute to the author's talented parents, both of whom produced outstanding works of art while living and raising a family under conditions reminiscent of earlier, pioneer times. Schintz imparts the flavour of the foothills with vivid and often humorous notes about neighbours, Bar U Riders, and the Stoney people, as well as describing the wildlife that has always contributed to the magic of Alequiers. A welcome addition to homesteading literature and social history, Alequiers will draw readers into the orbit of the daily life of a pioneering family who resided in one of Alberta's most prominent ranching districts with its whimsical and nostalgic journey into a recent, yet distant, past.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.006 |
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".