Speaking Notes for Mr. Calin Rovinescu, Executive Vice President, Corporate Development and Strategy, Air Canada, to the Calgary Chamber of Commerce Luncheon, Calgary, October 22, 2002.
Bibliographic record
Abstract
Calgary Chamber of CommerceGood afternoon ladies and gentlemen.It's a pleasure to be here and I thank the Calgary Chamber of Commerce for inviting me to join you today.I want you to know that there are people who try to discourage me from speaking in Calgary.Who knows?Maybe, after the next 20 minutes, you'll be amongst them.Nevertheless, my view is that it's important to let you, the business leaders of this city, know what is going on in our complex industry, what Air Canada is doing and why -and how that affects this city.That is particularly important at a time when the North American airline industry, in general, is suffering, like it has never suffered before.Instead of reeling off numbers and statistics, and at the risk of sounding too pessimistic, I thought I would start with a recent excerpt from a Reuters news report about the US airline industry: "How much uglier can the picture get for U.S. airlines?Industry watchers are ready to call in the plastic surgeon."I guess the plastic surgeon is better than the undertaker.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.386 | 0.219 |
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".