Bibliographic record
Abstract
Recent high-profile developments in the field of Green chemistry, such as the awarding of the 2005 Nobel Prize in Chemistry to Chauvin, Grubbs and Schrock for their development of a green organic-synthesis method, has brought Green chemistry to the forefront and highlighted the need to develop undergraduate programs, which teach the ideals of sustainable chemistry. An inorganic experiment is hereby presented which introduces Green Chemistry but is also correlated to many of the topics covered in the inorganic chemistry curriculum such as the hard/soft acid/base concept, charge density, ionic lattices, thermodynamics and molecular orbital theory. Alkali metal chlorides (MCl2n H2 O; M = Mg, Ca, Sr, Ba) are used to produce oxalate (M(C2 O4 )n H2 O; M=Mg, Ca, Sr, Ba) products through a water-based reaction. The use of Green solvents and methods to minimize waste are introduced as well as the topic of percent atom economy. These oxalates hydrates are characterized by thermogravimetric analysis and infrared spectroscopy and the results discussed in terms of the lecture material. Due to the limited expose levels of Sr and Ba, this experiment also provides a safe means to examine the chemistry of some heavy metals.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".