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Record W2098611422 · doi:10.4141/s05-118

Nutrient cycling in the vegetable processing industry: Utilization of potato by-products

2006· article· en· W2098611422 on OpenAlexvenueaboutno aff
E. Charmley, D. P. Nelson, Francis Zvomuya

Bibliographic record

VenueCanadian Journal of Soil Science · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsLivestockEnvironmental scienceNutrientAgricultureAgronomyBeef cattleFertilizerSolanum tuberosumNutrient cycleBy-productBiologyAnimal science

Abstract

fetched live from OpenAlex

Potato (Solanum tuberosum) production in Canada and the United States totals approximately 30 × 106 Mg yr-1. Approximately half of this is unsuitable for human consumption. This potato by-product comprises cull potatoes and potato processing waste (PPW). Liquid waste from processing plants can be applied to agricultural land. With strict environmental monitoring and control, crops such as corn (Zea mays L.), vegetables and grass can be used to divert large volumes of liquid waste. Solid waste and culls have traditionally been put in landfills or disposed of on agricultural land as a fertilizer. However these can be diverted from landfill sites or agricultural land and used as a high-quality animal feed, principally in beef feedlots. Research has shown that PPW can replace corn and barley (Hordeum vulgare L.) grain without negative effects on growth of beef cattle or meat quality. Indeed, efficiency of animal growth per unit diet intake is improved. These effects have been observed with diets containing up to 80% PPW. Results to date suggest that PPW is a valuable livestock feed ingredient and has no deleterious effects on beef quality. In areas where PPW is available, feeding to beef cattle represents a viable alternative to other disposal options. Key words: Potato, processing waste, by-product, cattle, land disposal

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.423

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.253
Teacher spread0.214 · 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 designObservational
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

Citations60
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Soil ScienceSame topicPotato Plant ResearchFrench-language works237,207