Techno-economic assessment of ear-hanging and lantern net grow-out techniques in Atlantic Sea scallop, Placopecten magellanicus, aquaculture in the Gulf of Maine
Bibliographic record
Abstract
Global scallop production has rapidly transitioned from a wild-capture fishery to an aquaculture industry over the past several decades. However, aquaculture of the Atlantic sea scallop ( Placopecten magellanicus ) in the Gulf of Maine has remained limited by the high labor burden and costs within the United States and Canada, particularly when using traditional lantern net culture. As a result, specialized ear-hanging equipment designed to automate husbandry processes is increasingly being employed in scallop aquaculture to reduce labor, despite the higher initial investment. Here, we used a techno-economic model to compare the cost of production, net present value, modified internal rate of return, lease size requirements, and labor-bounded maximum annual production of automated ear-hanging and traditional lantern net culture, production cycle duration, and market products. While ear-hanging entailed higher initial capital expenditures, it was notably more cost effective compared to lantern net culture; the advantages were compounded at larger production scales, longer production cycle durations, and when targeting an adductor muscle market. Labor efficiencies in ear-hanging related to a total annual production capacity of almost double that for lantern net culture and a lease acreage reduction of 40 % at comparable annual production. We recommend that growers looking to scale scallop production (>100,000 annual production) consider automated ear-hanging targeting an adductor muscle product. Meanwhile small-scale growers (<100,000 annual production) would likely need to adjust assumptions for a profitable business model. To assist growers in this decision-making process, we have included a scenario-testing application to adjust assumptions to fit their specific business requirements.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".