Non‐invasive ultrasound measurements for determination of hepato‐somatic index in <scp>Greenland</scp> halibut (<i>Reinhardtius hippoglossoides</i>)
Bibliographic record
Abstract
Abstract Hepato‐somatic index (HSI) is widely used to indicate fish body condition and level of energy reserves related to feeding/physiology and reproduction. HSI is traditionally obtained by dissection and measurement of liver mass versus body mass, but ultrasound technology may provide a minor invasive method to obtain liver dimensions to estimate the HSI of live fish. Images of Greenland halibut (Reinhardtius hippoglossoides) livers (n = 37) were obtained using ultrasonography, then subsequently dissected, weighed and measured using conventional methods. Four liver measurements (width n = 1 and depth n = 3) were obtained from ultrasound images and compared with corresponding physical width/depth measurements. The relationship between the derived ultrasound width/depth metrics and liver mass was then used to estimate total liver mass (LMU) and LMU values divided by the total body weight of each fish to derive the HSI values (HSIU). The LMU and HSIU values were not significantly different from the original liver mass and associated HSI values. Ultrasound liver depth was the most accurate metric for estimating HSIU values, but a degree of variability was observed. Derived HSIU values varied between maturity stages, matching predictions, but did not vary by sex or capture location. This non‐invasive method can be conducted quickly with minimal stress to fish, with applications to studies measuring HSI in commercially important to endangered species. The framework presented for measuring and estimating ultrasound‐derived HSI in live fish provides a baseline that can be improved with further validation work.
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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 | 0.000 | 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 | 0.000 |
| 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 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".