Bibliographic record
Abstract
Biking down South Access Road toward town, I breathe in the mountains, each a distinct character of rock, ridged or rippled. Grey Mountain dominates. Its slate top turns purple with the evening sun, and whitens gracefully as winter first approaches. The city sprawls carelessly astride the Yukon River. That big H on the far side marks the hospital, kiwi-green against the woods of Grey Mountain. Downtown squats the false-log frame of Klondyke Clinic. My place of work. The rhythm changes with each season. In winter, the dark comes quickly, but when the moon shines, I turn off my headlamp and ski in crisp and luminous night. In summer I finish at the office while the sun is still high, and take my sea kayak to the lakes. The kayak is emerald with an ivory hull, its bow turned up proud as a Viking ship. Close to town at Chadburn Lake, I can watch the beavers paddle their afternoon rounds. Just north is Fish Lake, windy and chillingly blue in its own mountain setting. Like most doctors, I make myself busy. But always within reach is the gently wild land that spreads out and forever. It's that beckoning outside that keeps me here. —
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.582 | 0.225 |
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".