The U.S. Arctic Observing Viewer: A Web-Mapping Application for Enhancing Environmental Observation of the Changing Arctic
Bibliographic record
Abstract
Although much progress has been made with various Arctic Observing efforts, assessing that progress can be difficult. What data collection efforts are established or underway? Where? By whom? To help meet the strategic needs of programs such as the U.S. Study of Environmental Arctic Change (SEARCH), the Arctic Observing Network (AON), Sustaining Arctic Observing Networks (SAON) and related initiatives, an update has been released for the Arctic Observing Viewer (AOV; http://ArcticObservingViewer.org). This web mapping application and information system has begun to compile the who, what, where, and when for thousands of data collection sites (such as boreholes, ship tracks, buoys, towers, sampling stations, sensor networks, vegetation sites, stream gauges, and observatories) wherever marine, terrestrial, or atmospheric data are collected. Contributing partners for this collaborative resource include the U.S. NSF, ACADIS, ADIwg, AOOS, a2dc, AON, ARMAP, BAID, CAFF, IASOA, INTERACT, and others. While focusing on U.S. activities, the AOV welcomes information exchange with international groups for mutual benefit. Users can visualize, navigate, select, search, draw, print, and more. AOV is founded on principles of interoperability, with open metadata and web service standards, so that agencies and organizations can use AOV tools and services for their own purposes. In this way, AOV will reinforce and complement other distributed yet interoperable cyber-resources and will help science planners, funding agencies, researchers, data specialists, and others to assess status, identify overlap, fill gaps, optimize sampling design, refine network performance, clarify directions, access data, coordinate logistics, collaborate, and more in order to meet Arctic Observing goals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.019 |
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".