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Record W4403947132 · doi:10.18280/mmep.111014

Enclosed Barn Conditioning System: A Comparison Between Traditional and Indirect Evaporative Cooling

2024· article· en· W4403947132 on OpenAlexvenueno aff
Marco Puglia, Saverio Mirandola, Michèle Cossu, Nicolò Morselli, Giulio Allesina, Simone Pedrazzi, Alberto Muscio, Paolo Tartarini

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2024
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsEvaporative coolerConditioningBarnAir conditioningEnvironmental scienceEngineeringMathematicsMechanical engineeringCivil engineeringStatistics

Abstract

fetched live from OpenAlex

Enclosed barns can be an effective farming system, providing proper animal welfare conditions to the cattle, with also positive effect on dairy productivity.On the other hand, the air conditioning process can be extremely energy-intensive and expensive, especially during summer months.This is not only due to the high air temperature and the solar irradiance, but also because of the metabolic heat produced by the cattle.The climatic condition considered for this study is the temperate-no dry season-hot summer, typical of northern Italy, where more than 80% of Italian milk is produced.Two conditioning strategies have been evaluated: the traditional chiller with a reverse thermodynamic cycle and an indirect evaporative cooling system based on the Maisotsenko cycle.The two systems were analytically compared considering an enclosed barn with a population of 500 lactating cows, highlighting the pros and cons of each systems.Traditional reverse cycle is capable of consistently reaching the chosen thermo-hygrometric conditions, ensuring the highest dairy productivity.However, this comes at the expense of significant energy expenditure.On the other hand, indirect evaporative cooling is not always able to reach the planned conditions, but it guarantees a significantly lower energy expenditure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.623
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.269
Teacher spread0.207 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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