Bibliographic record
Abstract
These lidar data show the passage of smoke from forest fires in Canada over the lidar site at Capel Dewi near Aberystwyth in Wales. A description of the event is provided in Vaughan et al, Atmos. Chem. Phys., DOI: 10.5194/acp-2017-1181. The files contain the photon-counting signals from the lidar as count-rate*height in km squared (corrected for background and pulse pileup), statistical errors in those signals, and synthetic molecular atmosphere profiles derived from two representative radiosonde stations in the vicinity. The counts have been integrated in time over a night, as follows: May23: 2148 on 23 May to 0309 on 24 May May24: 2126 on 24 May to 2206 on 24 May May26: 2304 on 26 May to 2344 on 26 May May29: 2111 on 29 May to 0331 on 30 May May30: 0036 on 31 May to 0257 on 31 May Cloud prevented night-long observations on these nights - cloud-free profiles were selected and combined by visual examination of the raw data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".