Bibliographic record
Abstract
The 3rd International Conference on Green Chemistry (ICGC-3) was held in Ottawa, Canada, 15-18 August 2010, with the theme “The Road to Greener Industry”. Bringing together academia and industry to trade ideas about green chemistry was the purpose of the meeting. Dedicated sessions on industrial aspects, presented by industry speakers, were well attended by both academics and industrial representatives. Academic sessions, in turn, presented new ideas to both groups. Major topics in the conference were green energy (biofuels, H2 production, CO2 capture), green engineering (energy efficiency, greener processes, separations), policy (industry, government, NGOs), green chemistry education, green transportation (materials, additives, powertrain) and green chemical synthesis (benign routes, solvents, catalysts, biopolymers). The 348 delegates travelled to Ottawa from 33 countries, making it a truly international discussion. This issue contains five important lectures from the conference. Peter Wells gives us a rather sobering discussion of some of the unintended consequences of green improvements. Zheng Cui, Evan S. Beach, and Paul T. Anastas describe many of the exciting green chemistry developments coming from China in the past three years. John Andraos announces a new database and algorithm that allows industry to evaluate the efficiency of synthesis plans. Achim Stolle and Bernd Ondruschka compare the performance and energy efficiency of solvent-free reactions performed by ball milling versus other methods such as microwave. Ken Seddon describes the use of new ionic liquids as catalysts for the oligomerization of linear terminal olefins to make lubricant oils. May these articles continue the discussion, stimulate more ideas, and help us all go down the Road to Greener Industry. Philip G. Jessop Conference Chair and Conference Editor
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.613 | 0.432 |
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".