Bibliographic record
Abstract
The Global Chemical Leasing Award was presented for the first time in March 2010 to organizations, companies and individuals for their outstanding efforts to enhance the visibility of Chemical Leasing around the world and reward successful Chemical Leasing initiatives and implementation. Chemical Leasing is the new business model in the field of sound use of chemicals, initiated and subsidized by the Austrian Federal Ministry for Agriculture, Forestry, the Environment and Water Management and jointly promoted with UNIDO. The decisive new aspect of this business model, which distinguishes itself from the traditional supplier-user relation, is to make the service performed by the chemical substance the basis of payment for the business operation, e.g. according to cleaned area, treated number of pieces, or performed hours of operation (= unit of payment). In this way an efficient use of chemicals is in the interest of all parties involved. The award was jointly organized by UNIDO and the Austrian Federal Ministry for Agriculture, Forestry, the Environment and Water Management. Organizations, companies and individuals worldwide were able to take part in the competition. The cases of the winners are described in detail and show the applicability of Chemical Leasing to the different industrial processes. Among these are water clarification and oil dehydration in Colombia, mineral water and beverage production in Serbia, oil & gas exploration and production and specifically deep gas field development projects in different places, industrial cleaning with solvents in Austria and textile dyeing in India.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.115 | 0.058 |
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".