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Record W2512668895 · doi:10.1038/ncomms12699

Photon management for augmented photosynthesis

2016· review· en· W2512668895 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNature Communications · 2016
Typereview
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsPhotosynthesisBiofuelBiochemical engineeringEnvironmental scienceSolar fuelAlgaeCyanobacteriaSolar energyArtificial photosynthesisProductivityPhotosynthetic efficiencyBiomass (ecology)BioenergyLight energyNanotechnologyChemistryMaterials scienceBiotechnologyBiologyEcologyBotanyPhysicsPhotocatalysisEngineeringBacteria

Abstract

fetched live from OpenAlex

Microalgae and cyanobacteria are some of nature’s finest examples of solar energy conversion systems, effortlessly transforming inorganic carbon into complex molecules through photosynthesis. The efficiency of energy-dense hydrocarbon production by photosynthetic organisms is determined in part by the light collected by the microorganisms. Therefore, optical engineering has the potential to increase the productivity of algae cultivation systems used for industrial-scale biofuel synthesis. Herein, we explore and report emerging and promising material science and engineering innovations for augmenting microalgal photosynthesis. Photosynthetic microalgae could provide an ecologically sustainable route to produce solar biofuels and high-value chemicals. Here, the authors review various optical management strategies used to manipulate the incident light in order to increase the efficiency of microalgae biofuel production.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
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 head0.048
GPT teacher head0.348
Teacher spread0.300 · 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