GROWTH OF <em>SACCHARINA LATISSIMA</em> IN AQUACULTURE SYSTEMS MODELED USING DYNAMIC ENERGY BUDGET THEORY
Bibliographic record
Abstract
Aquaculture is an industry with the capacity for further growth that can sustainably feed an increasing human population. Sugar kelp (Saccharina latissima) is of particular interest for farmers as a fast-growing species that benefits ecosystems. However, as a new industry in the U.S., farmers interested in growing S. latissima lack data on growth dynamics. To address this gap, we calibrated a Dynamic Energy Budget (DEB) model to data from the literature and a 2-year growth experiment in Rhode Island (U.S.). Environmental variables forcing model dynamics included temperature, irradiance, dissolved inorganic carbon (DIC) concentration, and nitrate and ammonium concentration. The modeled final estimate for S. latissima blade length (cm) was reasonably accurate despite underestimation of early season growth. Carbon limited winter growth due to a low modeled specific relaxation rate (i.e. the light-dependent reactions of photosynthesis) for some model runs; other model runs displayed nitrogen limitation which occasionally led to length overestimation and underestimation due to the degree of interpolation necessary from the field data. The model usage, however, is restricted to S. latissima grown in an aquaculture setting because of assumptions made about tissue loss, summer growth patterns, and reproduction. The results indicate that our mechanistic model for S. latissima captures growth dynamics and blade length at the time of harvest, thus it could be used for spatial predictions of kelp aquaculture production across a range of environmental conditions. The model could be a particularly useful tool for further development of sustainable ocean food production systems in the U.S. involving seaweed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".