Hostile bid rocks Inco as $63m China plant gets go-ahead
Bibliographic record
Abstract
An analysis was carried out on precipitation chemical data from 31 events in August and September 1978 and from June to October 1979, collected largely within a 50 km radius of the INCO Nickel Smelter at Sudbury. Ontario. During these periods INCO's daily SO2 emissions ranged from 658 to 2320td−1. and averaged approx. 1700td−1. With these emissions, it was found that the relative contribution of INCO emissions to the total wet-deposition of acids, sulfur and a number of trace metals in the Sudbury area (i.e. within about 50km of the smelter) is small (with the exception of copper and nickel), in the order of 10–20%, and depends on the weather system passing through the area. Warm fronts generally bring with them polluted air masses from Southern Ontario and the Eastern United States, and for acids, sulfur, and a number of trace metals, the INCO contribution to wet deposition in the Sudbury area appears to be about 10% of the total. For cold fronts, the percentage contribution from INCO is roughly twice as great. For copper and nickel, the smelter contribution appears to be roughly 40% of the total wet-deposition, regardless of the type of weather system. Nevertheless a definite influence of the smelter plume on the local downwind quality of precipitation can be detected, especially for sulfates and trace metals. The smelter impact on precipitation acidity is less pronounced. It was also found that during rainstorms, most particulate constituents (acids, sulfates, trace metals) are removed quite efficiently from the smelter emissions. Typically, almost 100% of these constituents may be removed within 50km during the rainy period. The percentage of the emitted sulfur that is removed by precipitation is much lower, mainly because this sulfur is largely in the form of sulfur dioxide which is subject to a low precipitation scavenging efficiency.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.323 | 0.063 |
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".