Plant community and litter composition in temperate deciduous woodlots along two field gradients of soil Ni, Cu and Co concentrations
Bibliographic record
Abstract
Perennial plant communities in the proximity of metal smelters and refineries may receive substantial inputs of base metal particulate as well as sulphate from the co-emission of sulphur dioxide. The Ni refinery at Port Colborne (Canada) operated by Inco (now Vale Canada Ltd.) emitted Ni, Co and Cu, along with sulphur dioxide, between 1918 and 1984. The objectives were to determine if vascular plant community composition, including standing litter, in twenty-one woodlots on clay or organic soil, were related to soil Ni concentration which decreased in concentration with distance from the Ni refinery. The soil Ni concentration in the clay woodlots ranged from 16 to 4130 mg Ni/kg, and in the organic woodlots, ranged from 98 to 22,700 mg Ni/kg. The concentrations of Co and Cu in the soils were also elevated, and highly correlated with soil Ni concentration. In consequence, each series of woodlots constituted a 'fixed ratio ray' of metal mixture exposure. For each of the woodlots, there were 16 independent measurements of 'woodlot status' which were correlated with elevated soil Ni concentration. Of the 32 combinations, there were eight linear correlations with soil Ni concentration, considerably more than would be expected by chance alone at a p-value of 0.05. With the exception of mean crown rating for shrubs at the clay sites, the correlations were consistent with the hypothesis that increased soil metal concentrations would be correlated with decreased diversity, plant community health or fitness, and increased accumulation of litter. Only five of the eight linear correlations were from the organic woodlots, suggesting that the observations were not confounded with soil type nor range in soil metal concentrations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".