Cloud-based data mapper (CDM): application for monitoring dry deposition of reactive nitrogen
Bibliographic record
Abstract
Spatiotemporal monitoring of reactive nitrogen atmospheric deposition is essential for understanding its impact on sensitive ecosystems and quantifying cumulative effects. However, the sparsity of direct surface flux measurements combined with barriers in dissemination are major limiting factors in providing this information to decision makers and non-experts in a timely manner. This work addresses both aspects of this information gap by, 1) utilizing satellite-derived reactive nitrogen dry deposition data products that can be used by decision-makers to supplement the sparse direct surface flux measurements and 2) fill in measurement gaps. Therefore, we have developed a Reactive Nitrogen Flux Mapper (RNFM) component of the interactive Cloud-based Data Mapper (CDM) for providing easy access of satellite-derived reactive nitrogen (defined here as nitrogen dioxide (NO 2 ) and ammonia (NH 3 )) dry deposition flux spatial maps/data to decision-makers/stakeholders over North America. The RNFM component of CDM has a Graphical User Interface (GUI) that allows users to specify the geographical regions and time periods for computing the average fluxes on the fly using an integrated cloud-based computing platform. The CDM architecture is flexible and can be upgraded in the future to take advantage of upstream satellite data directly on cloud platforms to provide results in near real-time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".