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Record W7025207044

From Unloved Woods to Diserable Renewable Biofuels

2017· other· en· W7025207044 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2017
Typeother
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyEnergy independenceFossil fuelGreenhouse gasBioenergyBiofuelBiomass (ecology)Raw material
DOInot available

Abstract

fetched live from OpenAlex

Increasing global demand for energy, a push by governments and industry to reduce greenhouse gases (GHG), and a desire to increase energy independence are driving the demand for renewable alternatives to fossil fuels. As a source of renewable carbon that can be used in the existing energy infrastructure, woody biomass is an attractive feedstock for the production of bioenergy (meant here to include biomassbased energy carriers in solid, gaseous and liquid forms) in the form of heat, power and liquid transportation biofuels.[...] Under the current forest harvest regimes in Ontario, Quebec, and Atlantic Canada, a substantial volume of dead, damaged, and low-grade trees go unutilized despite being part of the annual allowable cut (the government-dictated maximum harvest volume) or available harvest area. Forest operators leave these materials on site because they do not meet quality requirements for lumber and/or pulp production (Barrette et al. 2015). These types of low-grade trees are often too dry or too rotten, which restricts their suitability for the conventional forest industry (Barrette et al. 2012). For example, they may have been affected by fungi, cankers, cambial necrosis, trunk fissures or foliage loss.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.844
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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