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Record W2158066437 · doi:10.2174/1874331501408010001

Sustainability Indicators of Biomass Production in Agroforestry Systems

2014· article· en· W2158066437 on OpenAlexaboutno aff
Naresh V. Thevathasan, Andrew Gordon, Jamie Simpson, Xiaobang Peng, Salim N. Silim, Raju Soolanayakanahally, H. de Gooijer

Bibliographic record

VenueThe Open Agriculture Journal · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityEnvironmental scienceBiomass (ecology)BioenergySoil qualityBiodiversityAgroforestryEcosystem servicesSoil biodiversitySoil organic matterEcosystemEnvironmental resource managementBiofuelSoil waterEcologySoil science

Abstract

fetched live from OpenAlex

Production of biomass for bioenergy will depend on the sustainability of the production resource-base: the soil, water, air and the diversity of the ecosystem as a whole. For soil, potential sustainability indicators, including soil nitrogen and phosphorus, total organic matter, components associated with soil erosion and bulk density are discussed. Indicators related to the water resource water quality, water availability index, nitrate levels in water and biological oxygen demand are also discussed. Greenhouse gases and their sequestration potentials are discussed for maintaining atmospheric air quality. For biodiversity, a biodiversity index and soil biota index were selected as potential indicators. In addition to the production resource-base, the paper also discusses the importance of economic and social sustainability indices. To summarize, we are suggesting that a common method of visualizing the above indicated indices is to generate amoeba diagrams. This paper also provides a review on the term ‘sustainability’ and specific indicators, with metrics where possible, are described as candidates for inclusion as potential indicators appropriate for agroforestry based bioenergy systems in Canada. Three agroforestry based bioenergy systems are described and specific indicators are discussed within the bounds of these systems.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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 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

Citations19
Published2014
Admission routes1
Has abstractyes

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