Bibliographic record
Abstract
R Leonis On a bitter January night in frosty Montreal, I first watched R Leonis. A cold front had just passed through, leaving a crisp starry sky. Checking my variable-star chart, I began to look for R. It was frightfully cold. After an uncomfortable 45-minute search I finally found R Leonis as it rose through the haze and smog that hugged the eastern horizon. By this time I was so cold that even the simplest and smallest motions of the telescope were magnified into an agonizing exercise that taxed my whole being. I had finally found a faint magnitude 9.3 star, graced by two other stars – chambermaids assisting a stellar queen – at 9.1 and 9.6. It was so cold that the telescope tube froze to its mount and I couldn't even take the poor instrument inside! Quickly, observer minus telescope moved inside for some warmth. Never had hot chocolate tasted so good! Still outside, hundreds of light years away, shone my new variable. On that frigid night, R Leonis taught me two important lessons. One was that variable star observing can be challenging and worthwhile. The other is that to observe variables properly, one must first acquire a feeling for them, a genuine concern for what they are doing, and a will to undergo some discomfort to remain in touch with them. You may not feel this the first cold night out, but you will as you get familiar with the variable's behavior.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.141 | 0.064 |
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".