Bioelectrical Impedance Analysis to Estimate Lipid Content in Atlantic Salmon Parr as Influenced by Temperature, PIT Tags, and Instrument Precision and Application in Field Studies
Bibliographic record
Abstract
Abstract The objective of this study was to investigate the effectiveness of bioelectrical impedance analysis (BIA) in field studies monitoring Atlantic Salmon Salmo salar parr, as related to temperature corrections, instrument precision, and passive integrated transponder (PIT) tags. Currently, BIA studies are restricted to laboratory settings where water temperature is controlled to decrease error in BIA predictions caused by fish body temperature. We compared models of predicted total and percent lipids with and without temperature corrections and found that temperature corrections reduced error caused by temperature. Without temperature corrections, an 8°C increase in temperature increased the predicted total lipids by 55%. After temperature corrections were added, the predicted total lipid only increased by 2.55%. Repeated measurements were collected on 40 salmon parr (56–115 mm FL) in four separate time trials (1 min, 1.5 h, 3 h, and 6 h), and we found that lipid content predictions between measurements were not significantly different; however, the variability within longer time trials was moderate (6.43% error). No significant differences were found in the predicted lipid value before or after PIT tags were removed from the body cavity, suggesting PIT tags do not affect BIA readings. On average, the difference between predicted total lipids after tag removal was 0.0023 and 0.02 g for 12.5‐mm and 22‐mm PIT tags, respectively. We also observed that increases in fish body temperature caused by handing resulted in increased variability in BIA estimates, indicating the need for temperature corrections. Received March 10, 2014; accepted September 22, 2014
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".