The ARCTAS aircraft mission: design and execution
Bibliographic record
Abstract
Abstract. The NASA Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) mission was conducted in two 3-week deployments based in Alaska (April 2008) and western Canada (June–July 2008). The goal of ARCTAS was to better understand the factors driving current changes in Arctic atmospheric composition and climate, including (1) transport of mid-latitude pollution, (2) boreal forest fires, (3) aerosol radiative forcing, and (4) chemical processes. ARCTAS involved three aircraft: a DC-8 with detailed chemical payload, a P-3 with extensive aerosol payload, and a B-200 with aerosol remote sensing instrumentation. The aircraft augmented satellite observations of Arctic atmospheric composition, in particular from the NASA A-Train, by (1) validating the data, (2) improving constraints on retrievals, (3) making correlated observations, and (4) characterizing chemical and aerosol processes. The April flights (ARCTAS-A) sampled pollution plumes from all three mid-latitude continents, fire plumes from Siberia and Southeast Asia, and halogen radical events. The June-July flights (ARCTAS-B) focused on boreal forest fire influences and sampled fresh fire plumes from northern Saskatchewan as well as older fire plumes from Canada, Siberia, and California. The June–July deployment was preceded by one week of flights over California sponsored by the California Air Resources Board (ARCTAS-CARB). The ARCTAS-CARB goals were to (1) improve state emission inventories for greenhouse gases and aerosols, (2) provide observations to test and improve models of ozone and aerosol pollution. Extensive sampling across southern California and the Central Valley characterized emissions from urban centers, offshore shipping lanes, agricultural crops, feedlots, industrial sources, and wildfires.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".