Bibliographic record
Abstract
Goal. To determine the patterns of contamination adjacent to the motor road Kyiv — Odesa territories of the soil and the specificity of uptake by wild plants (zolotarnica canadian Solidagoсanadensis L.) pollutants. Methods. Field and laboratory studies. Results. The products of combustion of car engines moving along the freeway, there are a variety of chemical compounds, including metals — lead (Pb), chromium (Cr), cobalt (Co), and others. In soil samples taken at a distance of 5 m from the motorway, the presence of lead compounds was 11.401 mg/ kg, chromium — 19.361 mg/kg. At a distance of 1280 m from the roadway of the motorway in the soil was lead compounds 6,845 mg/kg, chromium — 5.376, cobalt — 0.271 mg/kg In the aboveground parts of plants of the canadian goldenrod (leaves, stems) high concentrations of the compounds were recorded in the samples that were selected at a distance of 5 m from the road: lead — 5.136 mg/kg, chromium — 6.366, cobalt — 3.158 mg/kg. At a distance of 5 m from the motorway in the underground parts of plants that are perennial organs, the concentration of lead compounds reached 2.763 mg/kg, chromium — 3.642, cobalt — 2.034 mg/kg. the distance from the motorway 1280 m recorded in the leaves of Canada goldenrod concentration of lead compounds in an average of 2.675 mg/kg, compared with the figures from the motorway (distance 5 m) 1.92 times, chromium — 1.614 (3.94 times less compared to the maximum accumulation in the experiments), compounds of cobalt — 0.165 mg/kg (in 19.1 times less). Conclusions. Accumulation of heavy metals in aerial parts of plants (leaves and stems) Canada goldenrod that grows near the road above (lead 1.86 times, 1.75 chromium, cobalt 1.55 times) compared with perennial underground parts of plants. The research results prove the feasibility of practical use of the canadian goldenrod as bioremediator contaminated soils of areas adjacent to roads with heavy traffic, and its sound practical economic use.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".