Bibliographic record
Abstract
According to the Collins English Dictionary Functional Water is defined as containing additives that provide extra nutritional value also called aquaceutical. Whereas conventional generally requires minimum chemical and structural changes perhaps with the exception of removing undesirable impurities and odours or tastes with targeted water treatment processes. Over the recent decades, both the scientific and bio-technological industrial communities have been promoting the benefits of functional water which possess novel altered or structured water characteristics to a wider acceptance in the contemporary developing world today. In its broader sense functional water can be defined as ... altered or structured water achieved by any mechanical, electrical, optical or other process or combinations thereof which alters the physical or chemical characteristics of water, thereby creating a new form or species of water which when utilised by plants, animals or humans demonstrates measurable and repeatable benefits to chemical, enzymatic and general cellular functions. This definition of :functional water goes beyond the general term of water. However, this presentation will focus on the utilisation of functional water in Australia and its implication to agricultural and food industrial and public health. In Australia, the utilisation of functional water particularly the electrolytic water in the above mentioned industries has started to gain momentum after a somewhat slow start. That said, one of the earliest application of electrolytic technologies is the so-called salt-water automatic cleaning system. The utilisation of electrodes and salt water for pool-water cleaning (sanitation) is now the most popular pool cleaning system in Australia. Australian love swimming. In Australia, there are more domestic swimming pools per capita than any other countries in the world. For example, with a population of about 23 million, Australia builds 20,000 new in-ground pools per year. This is in addition to about 800,000 existing pools. In comparison, USA with a population of 270 million installs only 90,000 new pools annually. In recent years, we have seen large commercial kitchen and catering industries using electrolytic water for both general cleaning and food sanitation purposes. There are also large scale field trials for pre- and post-harvest treatments of food crops using functional water which has gained the green tag and is regarded as organic farming practice. The market has also seen emerging boutique industries similar to that in Japan and Korea utilising functional water for direct health promotion. A precautionary measure would suggest that the long-term health benefits gaining from consumption of functional water directly or indirectly need proper validation. It would appear that the application of functional water is, to date, mainly utilised in developed countries such as Japan, Korea, USA and Canada. Significantly, the challenges remain how best the functional-water technology might be implemented for food security and water sanitation in developing countries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".