Commercialization of Irish moss aquaculture: the Canadian experience
Bibliographic record
Abstract
Abstract Irish moss traditionally has been valued for its hydrocolloid composition. Recognition that natural harvests would not meet the expected demands for its biomass led to experimental pilot-scale cultivation based on principles used in agriculture. Innovative technologies and systems for aquaculture management were devised when those from agriculture or mariculture were not directly transferrable. Periods of rapid progress and of consolidation due to disruptive external events were encountered, a cycle not uncommon during the introduction of a new technology. Certain key decisions in the background matrix that ultimately led to Irish moss cultivation are reviewed together with an overview of the main critical events that affected progress. The Chondrus crispus aquaculture as practiced today is essentially a modified form of precision agriculture operating year-round with c. 3.4 ha of on-land culture tanks and up to 75 employees during the peak season. Beginning with new Irish moss seedstock from the library/nursery, the crop is generated through a closely controlled, vertically integrated system of cultivation that after approximately 18 months increases the biomass more than 50,000-fold. After harvesting it is processed into the final food-grade products to meet the stringent demands of the export market.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".