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Record W2790243520 · doi:10.1002/9783527687596.ch6

Bioinspired Microfluidic Cooling

2018· other· en· W2790243520 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

Venuenot available
Typeother
Languageen
FieldEnergy
TopicSolar-Powered Water Purification Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFluidicsFlexibility (engineering)Wearable computerMicrofluidicsEngineeringThermal management of electronic devices and systemsThermalMechanical engineeringComputer scienceNanotechnologyMaterials scienceEmbedded systemAerospace engineering

Abstract

fetched live from OpenAlex

This chapter reviews various bioinspired approaches for the design of fluidic networks in wearable and architectural applications that have common elements to the fluidic mechanisms for biological thermal management. Fluidic cooling mechanisms for building windows can also benefit solar panel design. The chapter summarizes common manufacturing methods for wearable and architectural fluidic designs over large areas, where there are differing general requirements for scale, cost, contained flow, wearability, and mechanical flexibility. The fluidic designs for thermal management of buildings can be generally grouped into three main types: (i) thermal storage in fluidic layers, (ii) forced convection for thermal control, and (iii) fluidic networks for adaptive windows. The chapter also summarizes some of the fabrication methods for larger fluidic networks. It presents a review of various bioinspired approaches for the design of fluidic networks in wearable and architectural applications.

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 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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.049
Threshold uncertainty score1.000

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0710.022

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 head0.029
GPT teacher head0.293
Teacher spread0.264 · 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

Quick stats

Citations4
Published2018
Admission routes1
Has abstractyes

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