Bibliographic record
Abstract
Today much of a company’s competitive edge lies in the knowledge and competence found within the company. Technology and products are becoming increasingly complex meaning that the way knowledge is created and managed is extremely important. The Reactor Technology department at Alfa Laval launched the first of their continuous flow microreactors: ART ® Plate Reactors in 2007. The product differs from other more well-known Alfa Laval products, and sales engineers have the task of promoting and selling these reactors to customers around the world. How can sales engineers gain the knowledge and competence necessary for promoting and selling the reactors? What challenges exist and what additional sales and marketing tools are required for this task? A literature study of microreactors, knowledge management and adult learning was conducted. Furthermore semi-structured interviews with Alfa Laval staff, sales engineers and customers were carried out to identify existing needs and what was expected of the sales engineers. The role of the sales engineers was described as presenting and promoting the product to the customer, answering initial customer questions and being able to identify if a customer process is applicable to the plate reactors. The challenges described by sales engineers and the Alfa Laval reactor technology department included knowing which customer segments to focus on and
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.389 | 0.227 |
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".