2004 acid deposition assessment for Alberta : a report of the Acid Deposition Assessment Group /
Bibliographic record
Abstract
Acid deposition occurs when acid-forming pollutants emitted from anthropogenic and other processes undergo complex chemical reactions in the atmosphere and are deposited on the earth's surface.Management of acidic deposition requires an integrated approach that includes measurement and estimation of emissions and deposition, and evaluation of the effects of deposition on receiving ecosystems.Alberta Environment formed an Acid Deposition Assessment Group to provide inputs to an evaluation of provincial acidifying emissions and resulting acid deposition levels and effects in Alberta.This evaluation is required every five years under the Acid Deposition Management Framework.This report presents the current state of knowledge of provincial acidifying emissions and interpretations of resulting acid deposition levels and effects in the province of Alberta as part of the 2004 acid deposition assessment. Potential Acid Input in AlbertaThe REgional Lagrangian Acid Deposition (RELAD) model was used to predict annual potential acid input (PAI) in Alberta for the years 1995, 2000, and 2010 (projected).The Acid Deposition Assessment Group revised the receptor sensitivity map originally developed for Alberta broken down by grid cells.New receptor sensitivity data indicated that a Provost-Esther area grid cell 2004 Acid Deposition Assessment for Alberta -
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".