Bibliographic record
Abstract
Critical loads are a risk assessment approach that has supported large decreases in atmospheric acidic deposition globally. In Canada, SOx emissions fell by approximately 70 % between 1990 and 2021, whereas total particulate matter (TPM) emissions increased by about 40 %, mostly after 2010. Base cations are a major component of TPM, and critical load models consider base cation deposition as beneficial to ecosystems insomuch as it reduces the risk of acidification. However, close to point sources, high levels of alkaline dust deposition have altered soil chemistry and caused an undesirable shift in ecosystem state; something that critical loads are designed to prevent. In this study, the simple mass balance model (SMB) was modified with the objective of preventing base cation accumulation in soil above an acceptable threshold. The concept was applied to a forested site close to large emission sources of sulphur, nitrogen, and base cations in the Oil Sands region of Alberta, Canada. At this site, base cation leaching measured at 25 cm was approximately three times higher than estimated background leaching and exceeded combined SO4 + NO3 leaching. The critical load for alkalization was exceeded under each scenario considered in this study, although the exceedance was marginal if all N in current deposition was assumed to leach from soil. While this framework can easily be applied to regional and national critical load efforts, the main uncertainties of the proposed approach include base cation deposition estimates, assumptions regarding the behavior of N in soil, the selection of an appropriate Alkle(crit) and the long-term immobilization of deposited base cations in soil.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".