Factors Influencing Stable Isotopes and Growth of Algae in Oil Sands Aquatic Reclamation
Bibliographic record
Abstract
Previous studies reported (15)N enrichment of biota in reclamation wetlands that contain oil sands processed material (e.g., processed water and tailings); however, there is little information on the factors controlling (15)N enrichment in these systems. In this microcosm study, the aim was to examine stable C and N isotopes and growth (chlorophyll a [chl a] and dry weight) of algae as a function of exposure to different sources and concentrations of water-soluble fractions (WSF) derived from tailings. Two sources of tailings including mature fine tailings (MFT) and consolidated tailings (CT) and peat-mineral overburden were utilized to generate separate WSF that differed in water quality. In general, there was (15)N enrichment of filamentous algae along the increasing gradient of WSF/nutrient concentrations in both CT and peat microcosms, and among the different sources, algae were more (15)N enriched in CT WSF than in peat WSF. Growth of filamentous algae was inhibited at higher WSF concentrations, possibly due to reduced light availability at elevated levels of fine clay particles in MFT microcosms and colored dissolved organic carbon (DOC) in peat microcosms. Filamentous algae displayed lower biomass and (15)N depletion in 100% peat WSF. This study indicated that both the quality (source) and quantity of WSF affected algal growth and directly and/or indirectly influenced δ(15)N of algae. The distinct (15)N enrichment of primary producers derived from tailings suggest that stable N isotopes might be useful to trace exposure to oil sands processed material in biota that utilize these resources in reclaimed systems constructed with tailings or natural systems that receive tailings dyke seepage.
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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".