Adapting to a changing market: a business’s midlife shift in the Calgary market
Bibliographic record
Abstract
According to the U.S. Bureau of Labor Statistics; “about 50% of all new businesses survive 5 years or more, and about one-third survive 10-years or more.” Navigating the economic landscape is not an easy task for any business, even more so when your core business lies in the volatile industry of oil and gas exploration. The oil and gas industry fluctuates greatly on its own and when the market becomes oversaturated with the product, there is no need to invest greatly in exploration, so allocation of capital to this segment is restricted at the first signs of slowdown. Keeping in mind the volatility, this industry is very lucrative when oil and gas prices are high and exploration becomes a focus for most companies. With oil prices currently stabilizing after a major recession and economists predicting modest prices for the near future, most companies are expected to focus on process improvement and maximizing their return on their current assets as opposed to building more assets. The focus of this thesis will be on the company Seisware, which is a geophysical exploration software company based out of Canada’s major oil and gas market of Calgary, Alberta. With all of the changes in this industry in the recent years, major boom followed by a major downturn and finally what seems to be stabilization, how does Seisware continue to grow and maintain profitability in the future?
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 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".