National Atmospheric Deposition Program 2016 Annual Summary: 21 Years of MDN
Bibliographic record
Abstract
Highlights: NADP data were used in: 248 journal articles; six dissertations; five master’s theses; and twelve agency reports. The Mercury Litterfall Initiative with U.S. Geological Survey (USGS) scientists completed its fifth year of operation. Twenty-six sites collected litterfall (e.g., leaves, twigs, etc.) to measure mercury.• NADP continued its collaboration with USGS scientists to measure atmospheric isotopes of mercury at ~20 NADP sites. • The Total Deposition Science Subcommittee (TDEP) continued its work with U.S. Environmental Protection Agency (USEPA) scientists to estimate dry deposition of nitrogen, sulfur, and other analytes. • TDEP published a Map Summary for 2015. • TDEP continued to collaborate with Environment Canada to estimate mercury dry depo-sition using AMNet data. • TDEP was renewed as a Science Subcommittee for an additional four years.• NADP collaborated with Utah State University to monitor dry deposition as part of a pilot study.• The OTT Pluvio2-L raingage was accepted for network use.• The Aeroallergen Monitoring Science Committee was established.• NADP continued to work with the Council of State and Territorial Epidemiologists (CSTE) on a possible monitoring network for airborne allergen tracking. Airborne allergens are impor-tant as they contribute to allergic rhinitis (i.e., hay fever) and asthma
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.030 |
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".