The Role of Animals in Eco-functional Intensification of Organic Agriculture
Bibliographic record
Abstract
<p>Eco-functional intensification is understood as building synergies in multi-functional and resilient agricultural systems in harmony with their surrounding environment and human systems, to the benefit of diversified production of food and beyond, as in, for example, ecosystem services. Integration of animals into eco-functionally intensified agricultural systems to enhance agricultural, ecological and social systems, can contribute to driving a future sustainable development of organic agricultural and food systems. This approach may respond to challenges of an increasing industrialization of livestock in the global north, a process which has led to heavy reliance on external inputs, and, to a large extent, a detachment of animals from farming systems, especially in the global south. Animals are living sentient beings, but often not acknowledged as such. Complex, well-integrated systems can be organized so that they support the health and welfare of animals, and let these animals be valuable resources within the farming system. There must be an emphasis on diverse genetic inheritance and locally adapted species. Complex systems require complex knowledge which must be continuously developed to respond to current challenges in constantly changing environments, <em>e.g</em>., climate change. A necessary driver in transition towards more eco-functionally intensified agricultural and food systems is a governance system which protect the actors without a voice, <em>e.g</em>., ecosystems, pollinators, animals, and future generations through regulation of consumption patterns, flow of external inputs, and resource use. This requires a change in attitudes both acknowledgment of the importance of protecting the environment, consumption, resource use; and seeing agricultural systems as necessary and valuable contributors to nourishing both people and the earth.</p>
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".