Deliverable 2.12 Observational gaps revealed by model sensitivity to observations
Bibliographic record
Abstract
To understand the quality of the existing observing system in the Arctic to capture important elements of change over the Arctic we performed a gap analysis with respect to the Arctic Ocean, the Arctic atmosphere and the high-latitude carbon-monitoring network. The main points of the findings are: 1) The ocean observing system: The satellite altimeter system is a critical system to monitor the high-frequency variability. Due to the presence of sea ice in winter time, most of the area can be observed only every 5-10 days, leading to large observing gaps. Closing the gap can be done with new arrays of bottom pressure sensor ssuch as tide gauges or moorings in the ocean bottom. In addition, high-frequency transport measurements are required in the Fram, Davis Straights, the Barents Sea Opening, and north of the Laptev Sea. On the seasonal cycle, bottom pressure observations from GRACE are required to monitor the mass related variability and sea-ice observations are crucial for monitoring the halosteric related variability. On decadal time scales,it is important to have a sufficient hydrographic observing component capable of capturing temperature and salinity changes over the entire Arctic Ocean from the surface to the bottom. New algorithms that can recover sea level from sea ice covered areas may help to improve current satellite altimeter systems,and to improve the ability to monitor the Beaufort Gyre. 2) The atmosphere observing system: The density of the existing radiosonde observation network is not the most critical factor for the quality of T850 forecast. Instead,the results pointed out that stations on small islands in the middle of the Atlantic Ocean are critical for the quality of analysis. The Central Arctic Ocean and the Northern North-Atlantic would prob-ably benefit most from new sounding stations. Efforts to improve the quality of radiosonde observations, especially in Russia, would be very beneficial for the quality of T850 forecasts in the Arctic and sub-Arctic. Current data assimilation systems are probably not adequate to optimally exploit the information from the existing observational network. 3) GHG fluxes observing system: The existing network of pan-Arctic atmospheric monitoring sites provides continuous, well-calibrated observations on atmospheric greenhouse gas mixing ratios, generating basic information to quantify surface-atmosphere greenhouse gas exchange processes for most regions in Canada, Europe and Western Russia; also the Arctic Ocean receives good overall data coverage. Regions showing limited data coverage include the Russian Far East, Western Alaska, and the Eastern Canadian Provinces. Areas where footprint coverage gaps exist seasonally include parts of Western Russia and Central Siberia. Investments in observational infrastructure in any of these areas would be beneficial to increase the overall coverage of the pan-Arctic atmospheric network for greenhouse gases.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.270 | 0.132 |
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".