Effects of air mass origin on Arctic cloud microphysical parameters for April 1998 during FIRE.ACE
Bibliographic record
Abstract
Observations collected in April 1998 using the Canadian Convair‐580 during the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment‐Arctic Cloud Experiment (FIRE.ACE) are used to study cloud microphysics over the Arctic Ocean. Cloud microphysical parameters in climate models are specified as either constants or specific relationships based on cloud systems originating from either the ocean or land. The Arctic Ocean during winter and spring is mainly covered with ice. Because of this condition, the influence of the Arctic Ocean on cloud systems can be very different as compared to that of the midlatitude ocean. Air mass back‐trajectories calculated from the Canadian Meteorological Center (CMC) model outputs were used to define the origin of air masses as either the Pacific Ocean (PO) or the Arctic Ocean (AO). The uncertainty in specifying the origin of the air mass was less than 20%. The PO mean aerosol number concentration (Na) from the aircraft measurements was larger (108 cm−3) than for the AO cases (41 cm−3). The PO mean droplet number concentration (Nd) was 48 cm−3 in comparison to 77 cm−3 for the AO cases. The droplet effective radii (reff) for the AO and PO cases were 9.3 and 5.6 μm, respectively. Liquid water content (ice water content) changed from 0.05 (0.01) g m−3 for the AO cases to 0.13 (0.03) g m−3 for the PO cases. The averaged ice crystal number concentration was 20 L−1 for the PO cases and 10 L−1 for the AO cases. For April 1998, a statistical significance test on mean values at the 85% confidence level showed that the AO and PO cases had distinct microphysical and aerosol characteristics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".