An Assessment of the Impact of Antishattering Tips and Artifact Removal Techniques on Cloud Ice Size Distributions Measured by the 2D Cloud Probe
Bibliographic record
Abstract
Prior estimates of ice crystal size distributions derived from 2D cloud probes (2DCs) have been artificially amplified by small ice crystals generated from the shattering of large ice crystals on the probe tips. Although antishatter tips and algorithms exist, there is considerable uncertainty in their effectiveness. This paper examines differences in ice crystal size distributions from adjacent 2DCs with standard and antishatter tips, and processed with and without antishattering algorithms. The measurements were obtained from the National Research Council of Canada Convair-580 during the 2008 Indirect and Semi-Direct Aerosol Campaign (ISDAC) and the National Center for Atmospheric Research C-130 during the 2011 Instrumentation Development and Education in Airborne Science (IDEAS-2011). The 2DC size distributions are compared with those from the Holographic Detector for Clouds (HOLODEC), which has antishatter tips and allows for identification of shattering through spatial statistics.The ratio of the number concentration N of particles with maximum dimensions 125-500 m from the 2DC with standard tips to that from the 2DC with modified tips was correlated with median mass diameter and perimeter divided by area, but not with airspeed, attack, and attitude angles. Antishatter tips and algorithms reduced N by up to a factor of 10 for IDEAS-2011 and ISDAC, but neither alone removed all artifacts. For the period with coincident data, both N from the HOLODEC and 2DC with modified tips are around 5 x 10(-3) L(-1)m(-1), suggesting that antishatter tips and algorithms combined remove artifacts from the 2DC for the conditions sampled during IDEAS-2011.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".