Energy, water, plant interactions: ‘green feedback’ as a mechanism for environmental management and control through the application of phytotechnology and ecohydrology
Bibliographic record
Abstract
Abstract One of the fundamental tenets of the sustainable development concept is the maintenance of a homeostatic equilibrium within the ecosystem. Overexploitation of the ecosystem or degradation of its biotic structure alters ecosystem processes to the point whereby the ability of the ecosystem to produce desired resources is seriously diminished. Water is the medium for all ecological processes, from molecular to global scale. The physical quantification of ecological processes in terms of energy and molecular dimension is fundamental for scientific investigation and sound ecosystem management. As a consequence, the water mesocycle within the basin and connected processes (i.e. those which can be measured) should be considered as the basic unit for the analysis of energy–water–biota interactions. The degradation of freshwater ecosystems can be characterized in terms of two dimensions: first, pollution, which can be reduced to a significant extent by technologies; and second, the degradation of established water and nutrient cycles within the ecosystem as a whole. This second dimension is much more complex. A new approach for solving both of these problems is the application of ecohydrology and phytotechnology. Our progressive understanding of the range of anthropogenic degradation of hydrological, biogeochemical and biological processes within water basins indicates the need to control and regulate nutrients and water dynamics by increasing plant biomass and diversity. Thus, phytotechnologies, applied within the context of ecohydrological principles, can provide an approach for increasing ecosystem carrying capacity and enhancing the resilience and functionality of ecosystems at the basin scale. The application of these interdisciplinary, holistic approaches, based on an understanding of the role of plant biomass in the control of water and biogeochemical cycles (i.e. ‘green feedback’), can lead to an improvement of water resources quality, as well as the enhancement of system biodiversity, agricultural production, and bioenergy generation, with the potential for increased employment opportunities. Copyright © 2003 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".