Strain selection and temperature responses of Ulva and Ulvaria (Chlorophyta) for application in land-based cultivation systems
Bibliographic record
Abstract
The macroalgal family, Ulvaceae , holds promising candidates for cultivation in land-based Integrated Multitrophic Recirculated Aquaculture Systems (IMRAS) due to their fast growth and nutrient uptake capabilities. Selection of appropriate strains, however, is crucial before implementation in IMRAS. In this study, an evaluation of Ulvaceae strains was conducted through an initial screening of in total nine strains, eight sourced from natural habitats and one commercial Ulva producer. The abiotic conditions were characterised by high nutrient concentrations (883 μM NO 3 − -N) and were kept uniform for all strains during the screening. Following the initial screening, the effect of temperature on growth was investigated (10, 16, 22, and 28 °C) in two selected strains ( Ulva compressa and Ulvaria obscura ) under high nutrient conditions. This study demonstrated that four investigated Ulvaceae strains achieved high and stable growth rates (15–22% fresh weight d −1 ) in indoor free-floating cultures. Further, there was consistent and significant nitrogen uptake potential of a single Ulva compressa strain across temperatures between 10 and 22 °C (0.03 g N L −1 Week −1 corresponding to 74% of added dissolved inorganic nitrogen). Ulva compressa grew across all investigated temperatures with weekly variations in biomass yields (18–40 g dry weight m −2 day −1 ) while Ulvaria obscura grew stably at 10, 16, and 22 °C (15–16 g dry weight m −2 day −1 ). The findings of this study enhance our understanding of the potential uses of Ulvaceae strains in land-based cultivation and serves as a stepping stone for the integration of Ulvaceae cultivation into IMRAS on a commercial scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".