Modelling acidification, recovery and target loads for headwater catchments in Nova Scotia, Canada
Bibliographic record
Abstract
Abstract. The response of twenty acid-sensitive headwater catchments in Nova Scotia to acidic deposition was investigated for the period 1850–2100 using a dynamic hydrochemical model (MAGIC: Model of Acidification of Groundwater in Catchments). To ensure robust model simulation, MAGIC was calibrated to the long-term chemical trend in annual lake observations (13–20 years). Model simulations indicated that the surface waters of all twenty catchments acidified to the 1970s but showed subsequent recovery (increases in acid neutralising capacity (ANC) and pH) as sulphate deposition decreased. However, under proposed future emissions reductions (approximately 50% of current deposition) simulated ANC and pH will not return to estimated pre-industrial levels by 2100. An ANC of 20 μmolc L−1 and pH of 5.4 were defined as acceptable chemical thresholds (or critical chemical limits) for aquatic organisms in the current study. Under the proposed emissions reductions only one catchment is predicted to remain below the critical limit for ANC by 2100; three additional catchments are predicted to remain below the critical limit for pH. Dynamic models may be used to estimate target loads, i.e., the required deposition reductions to achieve recovery within a given time. Setting target loads at approximately 30% of current depositions would allow three of the four lakes to reach the chemical criteria by 2030. In contrast to the generally good prognosis for surface waters, soils lost an average of 32% of estimated initial base saturation and recovery is estimated to be very slow, averaging 23% lower than pre-acidification levels in 2100.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".