Bibliographic record
Abstract
The development of the chemical industry needs the development of chemistry and technical engineering. In the third quarter of the 19th century, organic chemistry in Britain didn`t develop compared to Germany. It was because, first of all, the leading groups of chemical industry didn`t properly make up in Britain, since educational systems lagged behind both quantitatively and qualitatively. Both on the science and the education, the German chemical industry gained competitiveness. Changing over swiftly to the public company, the German industry realized the economies of scale and scope. Specialized in chemistry, professional executives raised business effectiveness. In Britain, by contrast, the chemical corporations privately owned, the scope of its capital was small, and its effectiveness of management was insignificant. Entering into the synthetic dye, the German chemical industry was free from patent law`s constraints and freely copied the British synthetic dyes. In 1877, the German Patent Law was established. Since then the German chemical companies had instituted the world`s R&D laboratory, protecting their dyes that was developed by their R&D capacities and dominated the global market. From the third quarter of 19th century, the German chemical industry are enduring its tradition and competitiveness until now.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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".