Soil Fertility Management - Towards Sustainable Farming Systems and Landscapes
Bibliographic record
Abstract
Dr Maarten Stapper was a farming systems agronomist with CSIRO Plant Industry. He has lived, studied and worked in the Netherlands, Canada, USA, Iraq, Syria, and since 1982 in Australia. Maarten has an agricultural engineering degree in agriculture and catchment management in the semi-arid tropics, and a PhD in wheat production systems, linking crop physiology with agronomy and daily weather in simulation modeling. Quantifying production in dryland and irrigation wheat paddocks in southeastern Australia made him aware that most problems start with the soil, and thus solutions should commence there. Maarten is passionate about discovering and using the power of nature in food production systems – and the connections between soil biology, soil health, and the overall functioning of agro-ecosystems, and sees many opportunities for Australian agriculture to reverse soil degradation and regenerate soils. This feature in the CSIRO Sustainability Network Update (http://www.bml.csiro.au/susnetnl/netwl61E.pdf) is adapted from a presentation to the 3 National Organic Conference of the Organic Federation of Australia (OFA) in Sydney July 2006.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".