Bibliographic record
Abstract
A team of three young entrepreneurs formed the Auroma Brewing Company (ABC) in November 2014 right after their graduation from the University of British Columbia, Canada. Their first product was a smart coffee maker which could brew coffee with high precision, allowing users to experiment with variations in the brew. After successfully raising funds online in January 2016, they moved to Shenzhen, China, to manufacture the device. The initially promised shipment date was August 2017, but—as of October 2017—the backer and pre-order buyers were still waiting for their smart coffee maker. The team had faced several obstacles in outsourcing parts to various manufacturers, which caused several delays in shipping the device to the backers who had supported their project through the crowdfunding platforms. After the first delay, about one-tenth of the backers withdrew their supports. After successive delays, the remaining backers became more agitated and questioned the team’s ability to deliver the smart coffee device successfully. Detecting the market potential of ABC’s device, a large company had proposed to acquire ABC’s technology. The team wondered whether they should accept the offer or if they should explore other opportunities.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.016 |
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".