Nomenclature101.com: A Free, Student-Driven Organic Chemistry Nomenclature Learning Tool
Bibliographic record
Abstract
Fundamental to a student’s understanding of organic chemistry is the ability to interpret and use its language, including molecules’ names and other key terms. A learning gap exists in that students often struggle with organic nomenclature. Although many resources describe the rules for naming molecules, there is a paucity of resources available to actively and independently practice naming and drawing molecules and receiving feedback; plus, many of these resources are of low quality. Furthermore, students often do not see the real-life applications of the molecules they are naming. Our team created www.nomenclature101.com to respond to the learning gap and the lack of quality resources and to provide a link to real-life applications. This Web site hosts a free, interactive, online, bilingual (English/French) learning tool that draws from a bank of almost 1000 questions. This online learning tool is student-driven; it allows students to tailor their learning to their needs by creating customized nomenclature quizzes and providing immediate feedback. Nomenclature101.com has three key learning objectives; after working through the quizzes and other learning supports, students can (i) identify functional groups in a given molecule, (ii) name a molecule, given its structure, and (iii) draw a molecule, given its name. The learning tool is designed for high school chemistry and introductory organic chemistry courses. Students can use the tool independently or based on specific recommendations from their instructor; additionally, instructors could generate and print quizzes to use as part of their course assessment.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.183 | 0.108 |
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".