Lobster quality indicators for grading
Bibliographic record
Abstract
Abstract Male lobsters ( Homarus americanus ) harvested as ‘quarters’ (1.25 lbs/567 g) in two time periods (winter, N = 16, and summer, N = 25) of 2018 are examined. Indicators of quality data were collected for each lobster, including non‐invasive measures (shell hardness, carapace length, sex, live weight, colour, body shape, location, time of harvest) and invasive measures (blood protein [BRIX] level at time of harvest, cooked weight, meat content). Lobster BRIX levels are used as a proxy for actual meat content and as the key indicator of lobster quality. A regression model of the relationship between the natural logarithmic transformation of lobster BRIX levels (independent variable) and meat content yield as a percentage of shell‐on (uncooked) weight (dependent variable) is presented. The objective of this study is to evaluate alternative BRIX‐based decision rules for achieving desired meat content percentage yields for preparing shipments to global markets. The present study found that BRIX‐based grading rules can be determined to achieve minimum desired meat yields, minimum overall shipment yields and minimum proportions of shipments below desirable yield rate. For a minimum desired industry threshold of 24% meat content, the preferred BRIX value rules are (i) 8.5 mg mL −1 and (ii) ‘9 mg mL −1 with 10% plus condition’. These rules improve the admissibility of samples and reduce the risk of below desirable meat yields.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".