Trace elements in Ontario soils - mobility, concentration profiles, and evidence of non-point-source pollution
Bibliographic record
Abstract
Agricultural soils are the recipients of trace elements from general atmospheric pollution and from agricultural inputs such as fertilizer, feeds and urban biosolids. These input fluxes are usually small, and there are processes such as leaching and crop off-take to counterbalance the trace element inputs. Thus, it is difficult to evaluate the changes of trace element concentrations in agricultural soils. This paper examined a survey of 59 soil profiles in Southern Ontario, combining analysis of ~50 elements in three soil depths and corresponding measurements of the soil solid/liquid partition coefficient, Kd. The profile data were adjusted for yttrium concentrations to account for vertical particle migration. Increased concentration in the surface profile relative to the subsurface was considered an indication of enrichment, indicating the possible effects of human activity. For most elements, the surface (0–15 cm) and subsoils (30–60 cm) had similar concentrations. The notable exceptions were Cd, Pb, Sb, Se, Nb, U, and Zn, where surface soils had 1.4- to 2.2.fold higher concentrations than subsoils. Most of these increases can be attributed to human activity. Additional interpretation using the Kd data was useful to identify Ba and Mo as potentially among the contaminant elements. Surface soil concentrations of these elements were not markedly elevated compared with the subsoil, but their Kd values indicated that they were sufficiently mobile that depletion would be expected. Thus, perhaps continued input has supported the concentrations of Ba and Mo in the surface soils. Both are noted contaminants in dust from urban sources. Thus, the results show that several elements that are often of concern because of environmental toxicity or health impacts are at elevated concentrations in agricultural soils, and because these are rural locations the implication is that this has resulted from non.point.source pollution.Key words: Kd, partition coefficient, leaching, metals, cadmium
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".