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 Commerce Good 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. Calgary Chamber of CommerceNorth American airlines recorded losses of approximately U.S. $8 billion in 2001 and that is after $5 billion of direct U.S. government cash aid.In 2002, most observers expect that U.S. industry losses could exceed that.In the third quarter alone, US Airways filed for bankruptcy, United Airlines is on the brink and others are in dire straits.From airline to airline in the U.S., revenue is down because of low fares and less traffic, while costs -particularly labour and jet fuel -
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".