Bibliographic record
Abstract
This analysis of the air cargo market notes that air cargo volume soars when economies are healthy because the high cost of shipping by air is considered part of the cost of doing business when times are good, but that when economies contract, air cargo is among the first to be trimmed to lower costs. The near-future projections show low or flat growth in air cargo, with a recovery in late 2009, but only because late 2008 figures were so low. A major forecaster, Boeing Commercial Airplanes’ biannual “World Air Cargo Forecast,” calls it a “dire time,” and predicts that 2009 will be very tough, with some 18-24 months passing before a recovery. The September 2008 drop was the worst year-over-year decline since the dot.com collapse in 2001. With exports dropping, air cargo is also falling, as Chinese exports and U.S. imports have been the main drivers in recent years. Express operators are also expected to suffer declines. FedEx reported a 22 percent drop in net income for its most recent fiscal quarter. Longer-term, the Boeing forecast predicts air cargo traffic will grow 5.8 percent annually over the next 20 years. By 2027, it is expected to have tripled. It remains unclear whether this period of decline will be offset by above-average growth in demand.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".