A Meta-Analysis of Essential Amino Acid Requirements of Fish
Bibliographic record
Abstract
There are wide variations in the published estimates of essential amino acid (EAA) requirements. Variationsare thought to originate from different choices in mode of expression, response variable, and mathematicalmodel. Here we conduct a meta-analysis of the growth-based, dose-response trials of 10 EAA in 22 teleostspecies: 249 studies were reviewed. Published data were entered in a spreadsheet and re-calculated across in astandard and systematic manner to allow comparisons. The considered unit of requirement were percentage ofdry diet, g of EAA per MJ of digestible energy (DE), and g of ingested EAA per kg of metabolic body weight(MBW) per day. Response variables included growth in g per kg MBW per day and thermal-unit growthcoefficient (TGC). Four mathematical models were also compared: broken-lime model (BLM), quadratic model(QM), broken-quadratic model (BQM), and saturation kinetic model (SKM). Results first indicate importantdifferences in study quality, as 54% of the reviewed papers were excluded from the meta-analysis, often timesbecause of poor growth or missing information. Additionally, the final dataset was greatly fragmented: 31% ofthe studied concerned rainbow trout, and lysine was the focus of 29% of all studies, leaving some species andEAA poorly covered. Comparisons of the requirement estimates show important variations between studies,even within species. With such variability there was no difference in requirement estimates calculated withdifferent response variables. Similarly, this variability was not different between the three modes of expression,nor was it between mathematical models. However, there were significant effects of experimental design on thequality of fit of the models. Specifically, experiments that failed to produce a clear, plateauing dose-responsecurve had greatly increased probability to yielding absurd results (e.g. negative requirement). Finally, thepresent study emphasizes the critical need of a global, standard and systematic system to report and captureresults from nutrition trials.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.047 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".