Supplementary data for: Implementation of Dunaliella tertiolecta and Desmodesmus communis in a photobioreactor prototype for treatment of wastewater in a recirculating aquaculture system
Bibliographic record
Abstract
This folder contains supplementary data for the manuscript "Implementation of Chlorophycean microalgae in a novel photobioreactor for treatment of wastewater in a recirculating aquaculture system". This manuscript is currently under review in Current Research in Biotechnology, and the record will be updated with publication details upon acceptance of the manuscript. This research aims to support the application of microalgae in photobioreactors for aquaculture wastewater treatment by describing a plate-based screening tool to assess algal growth under diverse wastewater conditions, and describes growth of a microalgal species within a photobioreactor prototype. Two microalgal strains were subjected to an array of inorganic nitrogen sources. The plate wells were subjected to fluorescence microscopy and cells were counted at daily intervals over a 5-day monitoring period. Growth was modelled using Poisson regression models and growth rates compared between species and treatment conditions. Additionally, one algal strain was grown in a prototype photobioreactor under varied conditions, and comparisons between conditions were performed. The data presented here represents the raw cell count data, summaries of growth, and associated growth parameters (Poisson model B1 (growth rate) and average cell counts) for these experiments, as well as the R script used for creation of the Poisson models and for data analysis and visualization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.152 | 0.095 |
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".