Seed germination in tailings cake and cake-reclamation substrate mixtures with oil sands process water
Bibliographic record
Abstract
We investigated the germination of 13 species commonly used in oil sands mining reclamation of boreal forest as influenced by substrate type (potting soil, tailings cake and mixtures of cake-sand, cake-peat and cake-forest floor mineral mix (FFMM)) and water quality (0, 50 and 100% oil sands process water). Germination responses clustered into three groups with trees and graminoids exhibiting the highest germination (84-99%), followed by shrubs and forbs with intermediate germination (46-69%), and the native forb species, Chamerion angustifolium, Achillea millefolium and Galium boreale, with the lowest germination (7-18%). Among substrates, potting soil supported the highest germination (69%), followed by cake mixed with peat (64%) or FFMM (63%), cake-sand (60%) and cake (57%). Concentrations of ions, e.g. sodium and chloride, were higher in cake and cake-sand than in cake-peat or cake-FFMM suggesting that mixing cake with FFMM or peat can alleviate salt stress and encourage germination. Process water had little or no effect on germination especially on cake and cake amendments possibly due to the high ionic content of these substrates. There were major differences in germination response among species. Trees and graminoids may be well suited for reclaiming oil sands tailings whereas native forbs may perform poorly when used for revegetating tailings.
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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) | 0.001 | 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".