Bibliographic record
Abstract
Wiarton, Ontario Mar 8th '10 Der Mr. Muir Stickeen is one of my dearest dog friends. now, and forever, and I have quite a number of them, that, like him. I have never seen. "The wee hairy sleekit beastie": The little herd: and yet he was not heroic until the moment when, while still in deadly fear, he decided to face the peril, and deliberately slid his little feet over the edge of the ice diver. That moment should make him immortal. But the manner of manifesting his joy in being saved, exceeds any thin I have ever read of being shown by any creature below the human. Perhaps Browning was right when he said "God made all the creatures, and gave them our love and our fear To give sign we and they are His children, one family here" But the little book that your kind thought prompted you to send me has given me much more than the story of little Strickeen. It has given me not a glimpse merely, but a wide open gaze into the heart of John Muir, and I [illegible] that I have formed another friend, and not alone for the brief end of time that may remain to us here, but, for all the eternal years of God that lie beyond. Yet I reverently thank God for giving me the privilege of beginning the friendship while here. for it is so much the more gained, besides being an added joy to life 04725
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.917 | 0.893 |
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".