MétaCan
Menu
Back to cohort
Record W2533158144 · doi:10.11159/icesdp16.109

Product Environmental Footprint for Feed Production in Thailand

2016· article· en· W2533158144 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2016
Typearticle
Languageen
FieldComputer Science
TopicExperience-Based Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsFootprintProduction (economics)Ecological footprintProduct (mathematics)Computer scienceSustainabilityGeographyMathematicsEconomics

Abstract

fetched live from OpenAlex

Extended Abstract Food industry in Thailand is one of the high-volume exports. According to the statistics, in 2014, exports accounted for 27% of chicken production, which is ranked fourth in the world and ranked first in the EU (42%) [1], it presents the majority market share. This study aims to prepare and support Thailand industries to export to the EU single market by using product environmental footprint (PEF) to be a tool for evaluating the quality of products on environmental perspective. The study complied with methodology and environmental footprint impact category to conform to the PEF guide [2]. Also this study is a part of the shadow pilot project of the PEF, which led by the National Science Technology and Innovation Policy Office (STI) and National Metal and Materials Technology Center (MTEC) that collaborated with one of the large chicken industries named Betagro Public Company Limited. In a nutshell, the environmental impacts of chicken feed shall be assessed whole life cycle from cradle to grave, which covered from raw materials acquisition stage to disposal stage, by considering the functional unit was 1 kg of chicken feed products. The main ingredients of chicken feed should be provided the protein and energy to improve chicken health as with increasing in size and to produce more eggs. Maize, wheat, rice bran and soy bean meal are majorly used for chicken feed which contain high nutritive values, and therefore most widely used in animal feed industries because they contain less in fat and high in proteins. The composition of feed by each recipe based on age of chicken (breeder and broiler) and the growth stage of the chicken. The mainly environmental impact categories of chicken product has feed production (approximately value more than 50% by weight) and the significant environmental impact categories were climate change, freshwater eutrophication, freshwater ecotoxicity, water resource depletion and land use. The results found that the main contribution to environmental impacts of feed production is raw materials acquisition, especially maize from Thailand and soybean meal from Argentina. In worldwide typically use maize starch for main ingredient of poultry feed because its energy source is highly digestible for poultry. In addition, the plant protein source traditionally used for feed manufacture is soybean meal, which is the preferred source for poultry feed [4]. However, the environmental impact of broiler feed shows higher than breeder feed that because the ratio of maize in boiler feed to tonne feed is 1:0.46 whereas breeder feed 1: 0.54. Thus, this study focuses on the feedstuffs of broiler feed with percentage of protein is retained. The percentage of crude protein in feedstuffs as shown in table 1 [5,6].

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.628

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 science0.0010.000
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 head0.006
GPT teacher head0.185
Teacher spread0.179 · 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