Preliminary Results of Field Trials Testing Plants for Phytoextraction Capability in a Multi-metal Contaminated Environment Near a Lead Zinc Smelter A.G. Mattes, Nature Works Remediation Corporation*
Bibliographic record
Abstract
Screening of more than 100 members of the Brassica family and other species was completed at a University of Guelph greenhouse. Based on an extensive review of published reports plants were selected for future phytoremediation work on a site close to a smelter in Trail affected by particulate and plume deposition from stack emissions. The area chosen for research had been assayed and found to have high concentrations of many metals. Twelve plants were selected for field trials. Six replicates of five treatments were used: control; addition of biosolid residuals in a 50 % mix with existing soil; addition of 25 % peat, 25 % biosolid residuals and existing soil; and the same two treatments with the addition of Penicillium bilaii. Soil was sampled, and re-sampled after amendment addition. Plants were harvested at 6 & 12 weeks or when dead and assayed for metal content using ICP-MS. At harvest, a soil sample was taken from below the plant and assayed. Final harvest took place in October and plants were dried and weighed to provide an estimate of
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".