Strategies to Promote Value and Resilience in the American Lobster (Homarus americanus) Industry
Bibliographic record
Abstract
The American lobster (Homarus americanus) fishery is the most valuable single-species fishery in the United States and Canada (DFO, 2021; NOAA Fisheries, n.d.). Coastal communities on both sides of the border rely on the American lobster supply chain, which economically links the two countries with one another and with other countries around the globe. Ongoing disruptions (or shocks) threaten the functionality of the supply chain, and increased globalization leads to greater exposure to shocks as they are transmitted between systems. One research study described in this thesis aims to add value to the industry via post-harvest treatment of lobsters and the other aims to provide a greater understanding of the resilience of the supply chain. Added value and an improved understanding of the supply chain system’s resilience stand to help the industry better endure future disruptions. Chapter 2 of this thesis addresses methods of adding value to post-harvest, soft shell lobsters. Harder (higher grade) lobsters can be shipped further and sold for a higher price. A few days of storage post-harvest can allow soft-shell lobsters to harden. I use feeding, temperature, and ion supplementation treatments to attempt to increase lobster weight and shell hardness over the course of 5-day trials. I also compare the industry’s shell grading methods against durometer measurements. I found that the treatments used in this study did not significantly affect weight or shell hardness. The cold, ion-supplemented treatment prevented weight loss over time when compared with other treatments, however, it was also associated with increased mortality during transportation trials. Diets supplemented with minerals (such as calcium and magnesium) is a promising treatment which merits further research. More durometer testing is recommended to further examine overlap between grades, as such overlap may indicate that the industry is facing financial loss due to subjective grading practices. Chapter 3 of this thesis uses the COVID-19 pandemic as a case study to observe the resilience of the American lobster supply chain. I examine both the impacts of the pandemic on the supply chain as well as the responses of the industry. I use the food system resilience action cycle (Tendall et al., 2015) to frame this research and social media and formal interview datasets to understand how the sector adapted through the pandemic. This research elucidated the supply chain’s current resilience and how it may be made more resilient to future disruptions. This project also exposed potential disconnects between segments of the supply chain through the examination of maladaptations (responses which decrease system resilience). Those responses most effective in carrying the industry through the pandemic were found to be the result of collaboration between stakeholders and sectors: local support for stakeholders and a reliance on business relationships strengthened the industry in the face of disruption.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".