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Record W892594042 · doi:10.2429/proc.2014.8(2)044

Potential uses for solid biofuels from non-food crops

2014· article· en· W892594042 on OpenAlexaboutno aff
T. Ciesielczuk, J. Poluszyńska, Monika Sporek

Bibliographic record

VenueProceedings of ECOpole · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyNatural resource economicsBiofuelFossil fuelBiomass (ecology)Renewable fuelsAgricultural economicsEnvironmental scienceBusinessEnvironmental economicsWaste managementEconomicsEngineeringAgronomy

Abstract

fetched live from OpenAlex

The Directive 2009/28/EC on the promotion of energy from renewable sources (RES), sets mandatory national targets so as to be able to achieve a 20% share of energy from renewable sources in gross final energy consumption in the Community in 2020, the aim of the Polish is to achieve by 2020 a 15% share of renewable energy in gross final energy consumption. Thus, use of fossil fuels for energy production should you gradually reduced in favor of renewable energy sources. Usually, however, the change in the method of heating or design of installations using renewable energy sources to incur significant capital costs. In addition, the economic balance during the operation also sometimes detrimental to the modern great human and environmental technologies. Therefore, you should look for low-cost renewable fuels that may be used in particular in those households where there is no possibility of the use of gas or heat delivered from sources of power plants. This paper describes the possibility of using untreated plant biofuels. After the species were taken into account: Canadian goldenrod (Solidago canadensis L.) and mugwort wormwood (Artemisia absinthium L.). These plants are considered weeds have many advantages enabling wider use for energy purposes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.247

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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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 designBench or experimental
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

Citations6
Published2014
Admission routes1
Has abstractyes

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