Soil health – a perspective
Bibliographic record
Abstract
Governments and organizations are expressing growing concerns about soil health, driven largely by uncertainties of food security with an increasing human population and unpredictable effects of climate change. Although considerable literature and debate exist, there is discord around the question, what is a healthy soil? This is not surprising, given the complex roles the soil provides, from the range of food, fiber and medical products, hosting a biodiverse community, and supporting the water and nutrient cycles. While a consensus seems to suggest that a soil in good health should be able to provide goods and services in perpetuity, this does not define soil health, rather its provisioning functions. To explore the question, ‘what is healthy?’, we propose an analogy comparing indicators of human and soil health. For example, to identify the cause of a symptom, we compare the diagnostic pH in both humans and soil, demonstrating the similarities between the way human and soil health concerns are addressed. Additionally, we consider the context that necessitates health and use a set of holistic predictors to link human and soil health further. In humans, genetics express many traits and can predispose one to certain illnesses or diseases, in the same way, parent material, soil texture, and length of time exposed to weathering can inform a soil’s capability and predisposition for certain habitats or uses. In both cases, science informs the state of health and appropriate management solutions. We posit the null hypothesis “the concept of human health cannot be applied to soil”.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".