Screening of two freshwater green microalgae in pulp and paper mill wastewater effluents in Nova Scotia, Canada
Bibliographic record
Abstract
In recent years, the use of microalgae as feedstock for many marketable products, such as animal/aqua feeds, bioplastics and fertilizers, has gained renewed interest due to their fast growth potential coupled with relatively high lipid, carbohydrate and nutrient content. An algal biorefinery at an industrial site has the potential to sustainably and profitably convert carbon dioxide emissions into microalgal biomass and concomitantly reduce nitrogen and phosphorus from wastewaters. Industrial wastewaters are a potential alternative to traditional media used for large-scale microalgal cultivation. Pulp and paper mills are major consumers of water resources and discharge a huge amount of water to nearby lakes or rivers. This study investigated whether pulp and paper mill waste water is suitable for microalgal cultivation with the aim of achieving significant biomass production. Six different process waters from one Canadian pulp and paper mill were tested with two freshwater green microalgae. All of these waters were unable to support growth of microalgae due to inadequate nutrient concentrations, colour, turbidity and possible toxicity issues.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Bench or experimental | high |
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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".