Jack Pine Growth and Elemental Composition Are Affected by Saline Tailings Water
Bibliographic record
Abstract
In the processing of oil sands from Alberta's Athabasca formation, large quantities of alkaline, saline tailings and associated process-affected waters are produced. These waters may have a negative effect on plants used in reclamation of mined areas in this region of the northern boreal forest. In the present study, we examined the effects of process-affected water on the growth and elemental composition of jack pine (Pinus banksiana Lamb.) seedlings. Seedlings were grown in sand culture, and treated with tailings water to which mineral nutrients had been added. One-month-old seedlings were treated for 14 d, and all measured growth parameters were reduced. Growth and shoot elemental composition were also measured in seven-month-old seedlings that were treated for 10 wk with process-affected water. Shoots had significantly elevated levels of Na, Cl, S, P, B, and Sr, and significantly reduced levels of Fe, Mo, Ba, and K. The relationships between elemental composition and seedling growth and injury were examined using multiple regression. Growth rates, dry weights, and carotenoid content were reduced, but were not related to shoot elemental composition. Needle necrosis was positively related to tissue Na and Cl. Results indicate that reclamation planning must consider substrate Na and Cl levels when planting jack pine on tailings-affected sites.
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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 | 0.000 | 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".