Leadership in Sustainable Agribusiness, Innovation, and Solar Thermal Renewable Energy: Opportunities for Sustainable Agribusiness
Bibliographic record
Abstract
John Hollick, P.Eng, is the President of Conserval Engineering, a private company in Toronto with subsidiaries in Buffalo NY and Paris France. John is a Professional Engineer, an inventor, and a business person. His technology, SolarWall (SW) is one of the top 2% solar technology firms world-wide, as assessed by the US Government. It captures and makes conveniently available a very high proportion of solar radiation, over 90%, dwarfing the efficiency of photo voltaic panels (PV). The original technology is extremely sustainable requiring almost no maintenance, offering long term production with a life expectancy of several decades. Over the years, John has obtained several patents for his inventions, the last one awarded in December 2014. These patents reflect the continuous development of new technologies, new solutions built around the SW. Today SW offers solutions combining PV and thermal energy capture, or systems that may work to provide heating in the cold season and cooling in the warm season. All of the technologies are very cost effective. The case concerns possible agribusiness applications of one or more of the technology solutions developed by Conserval Engineering: the case users have been retained by John – thanks to their knowledge of agribusiness in their own country – to suggest possible applications of these technologies to local agribusiness industry. Mr Hollick has agreed to share a recent proposal (2010) to apply one of these technologies to solve the energy requirements of Food in Italy , while providing a significant carbon footprint reduction. He thinks that the analysis of this proposal and the material presented in the appendices will provide an opportunity to reflect on the potential of his technology and on the complex nature of the decision making process, and on some distortions resulting from public subsidies to some but not all sustainable technologies. That notwithstanding, the reflection on the first part of the case should reflect on the multiple gains that can be achieved with the adoption of these technologies in the specific local context. So, the case users armed with their reflections, their knowledge of agribusiness in their own country, and the short and essential description of the different technologies offered by Conserval Engineering, are asked to provide an illustration of what they consider the most promising applications to their own country’s agribusiness industry. Yes, the case does not provide actual costs of the different technologies, other than for the proposal to Food in Italy . At the same time the case users can certainly provide a qualitative perspective, based on the fact that these are the most advanced and cost effective technologies in the solar thermal industry. Part 1 of the case will primarily be structured around the discussion of these practical application to the local agribusiness. * Part 2 will focus on business sdevelopment. In using the case in teaching, parts 1 and part 2 could be linked to two different days.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".