Analysis of the variability of morphometric and external parameters of female kamloops trout using modern mathematical statistics methods
Bibliographic record
Abstract
The aim of the presented work was to study the variability of morphometric and exterior characteristics of female kamloops trout, to find the dependence of female weight on morphometric characteristics. Materials and methods. The research was conducted in November 2023 at the Parola fish farm located in Lake Ladoga. The object is three—year-old rainbow trout of the kamloops breed of Finnish origin. The planting material was brought to the fish farm in the spring of 2020 and was grown in cages. Upon reaching the age of three, 86 females with optimal phenotype parameters were selected and labeled from the total group of fish. The selected livestock was evaluated according to a number of indicators: external inspection, body measurements, morphometric and exterior. Studies of the variability of some morphobiological characteristics and body weight of female rainbow trout of the kamloops breed were carried out using modern mathematical statistics methods. Results. It was revealed that the normal weight of three-year-old female rainbow trout of the Kamloops breed is in the range of 4218...7595 g, the normal total body length is 62,2...74,4 cm, the normal commercial body length is 57,8...69,6 cm, the normal Smith length is 60.8...74.3 cm, the normal high-spin index is 31,3...38,2, the normal bighead index — 20,1...24,8, the normal fatness coefficient is 1,5...2,2. It is recommended to take this into account when carrying out breeding work and fish selection during reproduction. As a result of regression analysis, adequate power-law models were obtained that describe the relationship between the body weight of female kamloops trout and the main morphometric features: total body length, fishing length, body length according to Smith, the highest body height, the largest body girth, and head length, which can be used to predict the body weight of female kamloops trout. Conclusion. The results obtained make it possible to better assess the growth and changes in the linear weight and exterior parameters of fish during cultivation, and are also of great practical importance when carrying out breeding work with rainbow trout of the Kamloops breed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".