The atmospheric limb sounding satellite (ALISS)
Bibliographic record
Abstract
The Atmospheric Limb Sounding Satellite (ALISS) is a joint Canadian-Swedish concept that is currently under study by agencies, industrial partners and academic institutions in both countries. Launch is not anticipated before late 2020. ALISS has significant heritage, resembling the current Odin mission in terms of some of the countries involved and the types of instruments. However, ALISS will have a focus on the upper troposphere in addition to Odin's primarily stratospheric focus. The ALISS mission has objectives relating to climate-chemistry coupling, UV radiation, dynamics, atmospheric composition in the upper troposphere and lower stratosphere, and in conjunction with nadir sensors, air quality, by virtue of the array of key atmospheric constituents that it will measure with an unprecedented combination of vertical and horizontal resolution for satellite-borne instruments. ALISS consists of four atmospheric limb remote sensing instruments. Three of these have space heritage and are: the Canadian-designed Atmospheric Tomography System (CATS) that is a derivative of the highly successful Optical Spectrograph and InfraRed Imaging System (OSIRIS) instrument, the Swedish-designed Stratosphere Troposphere Exchange And climate Monitoring Radiometer (STEAMR) that is a follow-on instrument to the sub-millimetre radiometer (SMR) that currently operates with OSIRIS on Odin, and a Global Positioning System Radio Occultation instrument. The fourth instrument, also Canadian, is the Spatial Heterodyne Observations of Water (SHOW). SHOW will measure profiles of water vapour using its near-infrared absorption. Among other things, the ALISS package will deliver atmospheric composition (O3, H2O, NO2, HNO3, BrO, CO, aerosol, and others) measurements within the extremely important upper troposphere and lower stratosphere region for chemistry and climate studies. One application of interest would be using these measurements in conjunction with total column measurements from nadir-viewing instruments as well as data assimilation systems in order to better monitor and forecast air quality. Also, the heritage of these instruments implies the ALISS measurements will be extremely valuable in the continuation of climate-quality time series of important constituents such as stratospheric aerosols, water vapour, and ozone. Continuity of these vertically resolved data records is currently threatened by a looming gap in satellite-based limb sounders. This talk will outline the ALISS concept and the utility of the measurements.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".