MétaCan
Menu
Back to cohort
Record W2169240578 · doi:10.5539/jas.v7n4p68

Characterization and Recycling of Organic Waste after Co-Composting - A Review

2015· review· en· W2169240578 on OpenAlex
Zeeshan Anwar, Muhammad Irshad, Iftikhar Fareed, A. Saleem

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

VenueJournal of Agricultural Science · 2015
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCompostAmendmentStrawSawdustManureNutrientOrganic matterEnvironmental scienceAgronomyGreen wasteAerationBiodegradable wasteBiomass (ecology)Cow dungCrop residueWaste managementPulp and paper industryFertilizerChemistryAgricultureBiologyEngineering

Abstract

fetched live from OpenAlex

Co-composting produces a valuable compost material that can be used as valuable soil amendment. The process of the co-composting and control of the composting factors are the current challenges for the researchers. There are different factors that govern the quality, stability and the maturity of the co-compost in terms of amount of plant nutrients and reduction of heavy metals. Among these, C:N ratio is a parameter that can affect the loss of plant nutrients. Different studies showed wide ranges of C:N ratios (14-40) for maturity of quality compost. Temperature, aeration and types of the bulking agents also regulate the process of co-composting. Most widely used co-composted materials are animal manures with agro-wastes (sawdust, wheat straw, rice straw, corn stalks etc.). This practice brought substantial loss of heavy metals and maximum retention of plant nutrients. Higher nutrients contents of the compost and favourable soil properties as a result of co-composting of the saw dust, cow dung and egg shells have been reported. The application of co-composted dairy manure with wheat straw and sawdust produced higher plant biomass. Co-compost of cattle manure with rice straw produced an organic matter, total N and C:N ratio contents suitable for soil amendment. Therefore, this review focuses on the characteristics and utilization of organic waste after a reasonable co-composting process.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.865
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.049
GPT teacher head0.313
Teacher spread0.264 · 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