Bibliographic record
Abstract
Throughout my travels worldwide, studying and working at various universities and on different continents, including Europe, North America, Africa, and Asia, and teaching multiple organic chemistry subjects at the higher education level, I dreamed of compiling my organic chemistry insights into a single short textbook.It is a book that stands out among the hundreds of other organic chemistry books by being succinct, easy to read, and to the point.The book Organic Chemistry: 25 Must-Know Classes of Organic Compounds partially meets that dream.The book begins by discussing several functional groups in organic chemistry, followed by structure and bonding concepts.The book then goes on to introduce the 25 most important classes of organic compounds.These include alkanes, alkenes, dienes, alkynes, cycloalkanes, cycloalkenes, haloalkanes, aromatic hydrocarbons, aryl halides, phenols, alcohols, thiols, ethers, sulfides, heterocyclic compounds containing N, O, and S, aldehydes, ketones, carboxylic acids, carboxylic esters, acid anhydrides, acid halides, amino acids, fatty acids, nitriles, and organic polymers.The book delves further into topics and concepts of stereochemistry and also discusses the most wellknown organic reactions in a simple, concise, and straightforward manner while still fulfilling the learner's intellect.I hope that chemists, academics, and students enjoy reading this book.
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 | 0.004 |
| 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 | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.511 | 0.366 |
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".