Bibliographic record
Abstract
I was interested in your corrections, and gladly accept them with but onesmall exception: You have cut down the time it took you to get to me fromwhere you first saw me lying on the precipice from twenty minutes to twentyseconds. Yet you allow the sentence in which I speak of hearing you whistling and calling as you scrambled around the top of the slide and then down to my level. It certainly took minutes, and I thought I had your own computation of the time as we talked it over. I have compromised on ten minutes. I am deep in another story of the trail. I am counting the time till the shackles are off my hands and I can spend my whole time on this work. I am now taking care of three preaching places besides Cordova and am travelling almost half my time. By the way, have you read the poems of Robert W. Service? They are published in two volumes, under the titles The Spell of the Yukon and other Verses and Ballads of a Cheechako by Edward Stern and Co., Philadelphia. I consider Service the only real Poet of the North. Please write soon, and if you change your mind and come it will be all the better. Yours from the bottom of my heart, [illegible]
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 | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.758 | 0.659 |
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".