Refinement study of dry deposition inference methods used in Alberta /
Bibliographic record
Abstract
Acid deposition occurs when acidifying pollutants emitted from anthropogenic and other processes undergo chemical reactions in the atmosphere and fall to the earth as wet deposition (rain, snow, cloud, fog) or dry deposition (dry particles, gas).Acidic pollutants can be transported long distances in the atmosphere from their sources and eventually be deposited in ecosystems over broad regional scales and in locations far from the emission sources.Dry deposition is generally more a local problem than wet deposition.Direct measurement of dry deposition rates is difficult.Dry deposition depends on many factors, including: meteorological conditions, characteristics of the pollutants being deposited (e.g.different gaseous chemical and particle size), and characteristics of the surface on which deposition occurs.The most accepted and common method for estimating dry deposition is the so-called "inference method."The inferential method is a combination of measurement and modeling that involves indirect estimation of dry deposition rates on the basis of routinely measured air concentrations and meteorological parameters.The method is based on an assumed steady-state relationship F = Vd C, where the dry deposition flux or rate (F) is a product of the dry deposition velocity (Vd) and the concentration (C) of an airborne pollutant.A series of studies have been initiated by AENV to evaluate the inference method and search for the most suitable and simple model for deposition rate estimations in Alberta.This report documents the third study in the series.Titles for the reports of the other studies are: ''Review and Assessment of Methods for Monitoring and Estimating Dry Deposition in Alberta ", and ''Dry Deposition Monitoring Method in Alberta ".It is anticipated that once all necessary information is gathered, an Alberta protocol for dry deposition measurement will be prepared.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".