Bibliographic record
Abstract
Деловые людиGood afternoon!Please tell us about the activities and perspectives of your company in Russia.Good afternoon!Airgas was founded in the 1980's in the US with the purchase of a small distributor.Over the following 30 years, Airgas continued to grow organically and acquired over 500 independent distributors and businesses.Today, Airgas is one of the largest gases, welding and safety products suppliers in the US with a turnover of over 6 billion $. Do you have branch offices in other countries?Airgas offices are located in USA, Mexico, Canada and Russia.We have more than 1,000 offices in North America.Our company is a key distributor of such key manufacturers as: Lincoln Electric, Miller, 3M, Honeywell and other large vendors.In addition, Airgas produces industrial and spec gases at several facilities across North America.In May 2016, Air Liquide acquired Airgas and soon after begun integration efforts.The core business of Air Liquide is the production of industrial gases, as well as the development of related technologies.The activities of Airgas involve not only the production of gases, but also the sale of safety products, personal protective equipment (PPE), welding and cutting equipment and consumables and tools.This enables Airgas to provide a One-Stop-Shop offering to our existing and potential customers in every market segment.Air Liquide has decided to work together with Airgas in connection with the opportunity to build the same business model not only in North America, but in other geographies around the world, including Russia where they see tremendous opportunities for this approach.At the moment, we can see that this type of business model
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.389 | 0.248 |
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".