Estimating mineral surface area and acid sensitivity of forest soils in Kitimat, British Columbia
Bibliographic record
Abstract
In 2012, the Rio Tinto aluminum smelter in Kitimat, British Columbia increased sulphur dioxide (SO2) emissions from 27 to 42 tonnes/day. An initial study was conducted to investigate the effect of the increased sulphur (S) deposition on forest soils. A key uncertainty of the initial study was mineral surface area estimations that were applied to critical load calculations. The current study investigates the effect of organic matter (OM) removal techniques on mineral surface area and the ability to predict mineral surface area using pedotransfer functions (PTFs). Mineral surface area was measured on bulk soil samples using BET gas-adsorption. Organic matter was removed from soil samples prior to surface area measurements using a sodium hypochlorite treatment (NaOCl), loss on ignition (LOI) and no treatment. Removal techniques were found to affect surface area measurements; decreasing in the order of LOI> untreated> NaOCl. Particle-size based PTFs developed from other regions were not significantly correlated with measured surface area. A regionally-specific particle-size based function had stronger predictive value of surface area measurements (adjusted R2=0.82). The PTF that best reflected surface area measurements of bulk soil for the Kitimat area used particle-size data as well as kaolinite, the most abundant clay mineral in the region. Surface area values estimated using the particle-size PTF were applied to the PROFILE model to calculate weathering rates. Weathering rates were then input to critical load calculations using steady-state mass balance. These estimates predicted that none of the 24 measured sites are receiving SO2 deposition in exceedance of their critical load. Author Keywords: acid deposition, critical loads, mineral surface area, mineral weathering, pedotransfer functions, PROFILE
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".