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Record W4415820467 · doi:10.19189/001c.129426

An Exploration of Common Reed ( <i>Phragmites Australis</i> ) Bioenergy Potential in North America

2014· article· en· W4415820467 on OpenAlexaffabout
Regina Vaicekonyte, Erik Kiviat, F. Nsenga, A. Ostfeld

Bibliographic record

VenueMires and Peat · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsFrancophone University Association
FundersUniversität GreifswaldBard CollegeNational Science Foundation
KeywordsBioenergyContext (archaeology)Biomass (ecology)BiofuelBiodiversityHabitatEnergy cropEcosystemVegetation (pathology)

Abstract

fetched live from OpenAlex

In North America, reed ( Phragmites australis ) is typically considered to be a weed although it provides important ecosystem services. Small, sparse, patchy or mixed reedbeds are more suitable as habitat for many species than extensive dense reedbeds, whose habitat functions can be enhanced by the selective removal of biomass. We propose that above-ground reed biomass could be harvested for bioenergy, at the same time improving habitat for biodiversity by thinning or fragmenting the more extensive reedbeds. Biofuel pellets manufactured from reeds harvested at Montréal (Canada) had moisture content 6.4 %, energy content 16.9 kJ g -1 (dry mass), ash content 3.44 %, and chloride content 1962 ppm. Thus, reed as a material for fuel pellet manufacture is similar to switchgrass ( Panicum virgatum ), which is commonly cultivated for that purpose and requires higher inputs than harvested wild reed. We discuss these findings in the context of environmental considerations and conclude that the bioenergy potential of reed could most expediently be realised in North America by combining material harvested from the widespread spontaneously occurring reedbeds with organic waste from other sources to create mixed biofuels. However, reeds with high levels of chlorine, sulphur or metals should not be burned to avoid air pollution or equipment damage unless these problems are mitigated by means of appropriate season of harvest, equipment, combustion regime, or use of a mixed feedstock.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.892

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 head0.022
GPT teacher head0.223
Teacher spread0.202 · 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 designOther design
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

Citations0
Published2014
Admission routes2
Has abstractyes

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