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НОВЫЙ КОМПЛЕКСНЫЙ ИНДЕКС ПЛЕМЕННОЙ ЦЕННОСТИ КРУПНОГО РОГАТОГО СКОТА RZ€ И ЕГО ПРИМЕНЕНИЕ В ГЕРМАНИИ (обзор)

2021· article· ru· W4405313695 on OpenAlexaboutno aff
Ashraf Rakhimov, А.Б. Тлеубаев, С.Ш. Сатыгул, В.К. Дмитраш, Н.С. Садуов

Bibliographic record

VenueProblemy biologii produktivnyh životnyh · 2021
Typearticle
Languageru
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexIndex (typography)Profit (economics)Agricultural economicsAgricultural scienceMilk productionMathematicsStatisticsEconomicsAnimal scienceEnvironmental scienceBiologyComputer scienceFinanceMicroeconomics

Abstract

fetched live from OpenAlex

Экономическая эффективность производства молока определяется как производственными факторами, так и генетическими особенностями животных. В странах с развитым животноводством при разведении голштинского скота используются индексы племенной ценности животных: в США – TPI (Total Performance Index), в Канаде – LPI (Lifetime Profit Index), в Германии – RZG (Relativzuchtwert Gesamt). Используются также экономические индексы ценности молочного скота – в США индекс прибыли LNM$ (Lifetime Net Metric), в Канаде – $Pro (Pro Dollars), в скандинавских странах – индекс рентабельности NTM (Nordic Total Metric), в Великобритании – национальный индекс пожизненной прибыли £PLI (Profitable Lifetime Index). С августа 2020 года в Германии для оценки экономической эффективности разведения крупного рогатого скота голштинской породы в дополнение к общему относительному индексу племенной ценности RZG был введен новый комплексный индекс – RZ€ (Relativzuchtwert Euro). В отличие от индекса RZG, он учитывает влияние показателей здоровья животных на молочную продуктивность в стоимостном выражении. Кроме того, в RZ€ не учитывается оценка показателей экстерьера и содержания соматических клеток в молоке. Цель данной работы – обзор основных аспектов, оказавших влияние на развитие молочной отрасли за ряд последних лет, и послуживших в качестве обоснования разработки и внедрения индекса RZ€ в производственное использование в Германии. ABSTRACT. The economic efficiency of milk production is determined by both production factors and the genetic characteristics of animals. In the main countries with developed animal husbandry, the breeding of Holstein cattle has been carried out for a relatively long time according to the indices of the breeding value of animals. In the United States of America, this is TPI (Total Performance Index), in Canada - LPI (Lifetime Profit Index), in Germany - RZG (Relativzuchtwert Gesamt), etc. Along with them, economic indices of the value of dairy cattle have found their application since a certain time. For example, in the United States of America, the LNM$ (Lifetime Net Metric) lifetime profit index, in Canada - $ Pro (Pro Dollars), in the Scandinavian countries - the NTM (Nordic Total Metric) profitability index, in the UK - the £PLI [Profitable Lifetime Index] national lifetime profit index are used. Since August 2020, in Germany, in order to assess the economic efficiency of breeding Holstein cattle, in addition to the general relative index of breeding value RZG, a new complex index has been introduced - RZ€ (Relativzuchtwert Euro). It differs from the already used RZG index in that it takes into account the influence of animal health indicators on their milk productivity, which can be expressed in economic value terms. In addition, the RZ€ does not take into account the assessment of the conformation indicators and the number of somatic cells in milk. The aim of this paper is to review the main aspects that have influenced the development of the dairy industry over the past several years, and served as a rationale for the development and implementation of the RZ € index in industrial use in Germany.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0030.004
Scholarly communication0.0100.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0330.013

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.

Opus teacher head0.045
GPT teacher head0.235
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

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Citations0
Published2021
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