Analysis of BIoT-Based Energy Business in the City of Montreal—Lessons Learned for Broader Markets
Bibliographic record
Abstract
The internet of things (IoT) is considered one of the fastest-growing topics attracting the attention of the technological world. Building internet of things (BIoT) focusses on the IP-marked components of a building, connected to the network (internet), generating and collecting data, or executing orders autonomously and sim-autonomously. BIoT encompasses various classes of applications including energy, water, building automation, security and fire protection. The success of BIoT solutions, however, relies highly on the business outcomes expected from implementing them. The present study focuses on building energy solutions (and the associated building automation applications). In an attempt to formulate a high-level and generic business model, this paper uses Osterwalder business canvas as a framework, and compiles extensive number of case studies in the city of Montréal, QC. Experts opinions are sought through survey questionnaires to enhance, validate, and verify the model, and provide an overview of the BIoT market in Montreal. Our model provides an opportunity to explore other classes of BIoT and move towards formulating generic business model canvas for IoT in building sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".