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Record W1980975122 · doi:10.1145/2768510.2770944

The Spot* System for Flexible Personal Heating and Cooling

2015· article· en· W1980975122 on OpenAlexaff
Alimohammad Rabbani, Srinivasan Keshav

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsASHRAE 90.1Hot spot (computer programming)Blind spotComputer scienceSoftware deploymentSimulationSpot contractThermal comfortAutomotive engineeringEnvironmental scienceEngineeringMeteorologyArtificial intelligenceOperating systemPhysics

Abstract

fetched live from OpenAlex

SPOT* is a cost-effective, legacy-compatible, flexible system for personalized heating and cooling. It senses occupancy and worker comfort uses the Predicted Mean Vote equation to determine worker comfort. It then actuates a fan or a heater to adjust the comfort level so that it lies between -0.5 and +0.5 in the ASHRAE comfort scale. SPOT* greatly reduces costs compared to our prior SPOT and SPOT+ systems by using the fewest possible sensors and a lightweight compute engine that can be located in the cloud. Moreover, SPOT* provides both cooling and heating using a speed-controlled desktop fan, rather than only controlling heating using a radiant heater. Finally, SPOT* is less intrusive in that it does not use a camera. The per-user cost for SPOT* is about $185 compared to $1000 for SPOT/SPOT+. We find that in a preliminary deployment, SPOT* is able to improve user comfort by 78% over legacy systems alone.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.059
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0590.013

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 head0.019
GPT teacher head0.213
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 source (direct Gemma or distilled Codex), 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

Citations3
Published2015
Admission routes1
Has abstractyes

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