Bibliographic record
Abstract
2 That would be a mistaken view. It is only my old fashioned economy showing up That visit is a choice subject for my imagination I see your house and ranch I used to imagine myself on a mountain journey with you on foot of course for that must be the real way to travel the mountains but I cannot allow my mind to get so far from the practical now my lameness cuts that out I guess I shall see mountains enough getting there I should like to go by the Canadian Pacific. I am told that the grandest mountain scenery is on that route. There are also old friends along the way and the big mills of British Columbia and Washington. Have some relatives too on the way to California so I can at least plan a splendid journey for the old man You will think it a miserably small one after going around the world. Let me tell you about this $100. from Charlie. It was very fully told once, more than a year ago in the lost letter. It is a little singular that of my very few lost letters two of them should be to you the former one was when you were beginning your mountain travels It takes away the zest of writing to tell things twice or to think they might not possibly land all right The morning you were leaving us here in Milwaukee you inquired regarding Charlie and said that for his sake you would like to see the $100 I understand you like myself you hate to lower your estimation of 03421
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.910 | 0.861 |
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".