Bibliographic record
Abstract
This article describes details of classes at Dalhousie University in 1868–1869, of the life of George Lawson, the first Professor of Chemistry and Mineralogy, and of the wide range of chemical concepts known at that time. A comprehensive set of lecture notes from Lawson’s chemistry course, written by a student, Alexander Russell, and held in the Dalhousie University Archives, offers a wonderful insight into the state of chemical knowledge and how it was taught at that time. Lawson began with general chemical principles followed by a detailed discussion of the nonmetals. The second half of the class covered a range of metals followed by a small section on mineralogy and a large section on organic and biological chemistry. Lawson used an older set of atomic masses in which many, but not all, of the elements had masses one-half of the accepted values today. When corrected for these errors, Lawson’s formulae, even for complex molecules such as morphine, mostly agreed with contemporary usage. Examples of nomenclature, chemical formulae, preparations, processes, and properties are presented. A few examination questions are given also. Even though the concepts involved in understanding chemical structure were just being developed, the breadth and depth of descriptive chemical knowledge at that time was remarkable.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.229 | 0.054 |
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".