OASIS-CANADA CFL: Atmospheric composition and structure during the CFL campaign from the Amundsen icebreaker in the Amundsen Gulf
Bibliographic record
Abstract
Data were compiled in campaign mode and were obtained over the ice of the Amundsen Gulf. Chemical and meteorological variables were collected with commercial and custom made instrumentation, as detailed below. Ozone (O3): Measurements were taken with TECO TEI049 instrument and five-minute averaged data with filtered generator influence are archived in one file. Wind components and their vertical profile: Wind data were collected by Sodar model Scintec MFAS SN A-C-0073 with software version APRun 1.13. Reported data are at interval of 0.25hour. GPS record is embedded into Sondar data record. In addition, the following data sets have been created by collaborators: (1) Zone vertical profile with home built differential absorption LIDAR (Jim Whiteway, York University, Toronto, whiteway@yorku.ca); (2) BrO MAXDOAS (multiaxis differential optical absorption spectroscopy) and LPDOAS (long pass active differential optical absoprtion spectroscopy) (Udo Frieß and Denis Poehler, University of Heidelberg, Germany, udo.friess@iup.uni-heidelberg.de, denis.poehler@iup.uni-heidelberg.de); (3) BrO by chemical trapping followed by GC/EC analysis (Paul Shepson, Purdue University, USA, pshepson@purdue.edu). GPS locations are available in D. Barber et al., Atmosphere-Ocean 48, Issue 4, pp. 225-243, 2010.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".