Bibliographic record
Abstract
This article is a profile of Toronto-based Porter Airlines, which is a regional startup in Air Canada’s primary hub city. But instead of Toronto Pearson International, where the competition is fierce, Porter is flying out of Toronto City Centre Airport, where it is the sole scheduled airline. Located just minutes from downtown, it is attractive to business travelers or day-trippers to one of the half-dozen cities Porter serves with around 50 daily arrivals and departures using six Q400 turboprops. Privately owned, it does not release earnings figures, but officials say they established a net income margin of eight percent and have come out of their first full year of profitability. A wholly owned subsidiary of Porter Aviation Holdings, it was started with $125 million in capital, invested by industry veterans and venture capitalists. Porter’s monopoly at City Centre and its operation of commercial flights out of an airport located so close to residential areas have draw complaints from regulators and the public, but it continues to invest and expand. Flights to the U.S. are expected to grow from the current service to Newark. Porter offers amenities such as free shuttle service between downtown and the ferry that carries people to the island on which the airport is located. Porter’s success and high load factors have enabled authorities to levy fees to fund airport improvements, and the airline intends to more than double its fleet soon.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 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".