EXPLORING <i>PORPHYRA</i> SPECIES FOR USE AS NITROGEN SCRUBBERS IN INTEGRATED AQUACULTURE
Bibliographic record
Abstract
Carmona, R.1, Kraemer G. P.2, Zertuche, J. A.3, Chanes, L.4, Chopin, T.5, Neefus C.4,6 & Yarish, C.1 1Dept. of Ecol. and Evol. Biol., University of Connecticut, One University Place, Stamford, CT 06901, USA; 2Department of Environmental Sciences, State University of New York, Purchase, NY 10577 USA; 3IIO, Universidad Autonoma de Baja California. Ensenada,B.C., Mexico; 4DGETI‐CBTis41, Mexico; 5CCSA, Dept. of Biol., University of New Brunswick, Saint John, N.B., E2L 4L5, Canada; 6Department of Plant Biology, University of New Hampshire, Durham, NH 03824, USA Finfish mariculture along the Northeast US coast continues to develop into a strong industry. At a regional level, mariculture can be a significant contributor to nutrient loading in coastal waters. Since macroalgae are able to concentrate nutrients and grow at high rates, they can be an useful tool for alleviating this problem. In addition, seaweed mariculture is by itself a multi‐billion dollar industry, with the red alga Porphyra (nori) valued at over $US 1.8 billion. Local species and strains of Porphyra from the Northeast U.S.A. are being studied to determine their capacity as nutrient scrubbers under different nutrient and temperature conditions. P. purpurea was grown under two N sources (NO3‐ vs. NH4+). The fastest growth (up to 13% d‐1) and greatest N content (ca. 7% DW) were measured in plants grown at 300 µM NH4+. Short‐term NH4+ uptake by P. purpurea (strains from Maine and Long Island Sound) and by P. amplissima was not saturated at 150 µM, the highest concentration tested. The P. purpurea isolate from Maine took up NH4+ faster than did the Long Island Sound isolate. NH4+ uptake by P. amplissima was faster than uptake by either P. purpurea strain. The high growth rates obtained and the ability for N uptake and tissue accumulation make these species suitable for using as a biological nutrient removal system.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".