Prairie Soils & Crops Journal
Bibliographic record
Abstract
Soil is, without question, critical to the world, supplying virtually all the food and fibre that sustain the human population, and providing ecosystem services that support life. The world’s arable land at 1.35 billion hectares seems vast, but is only 0.20 hectare per person, not evenly distributed. Africa and Asia, for example, have 46 % of the arable land and 71 % of the population and a dominance of low quality land with weathered and infertile soils. The world’s more developed countries in North America and Europe not only have more land per person, but higher quality land and more resources for soil conservation. Conservation is essential with all lands. Despite much progress with modern practices such as conservation tillage, the problem of land degradation is serious particularly in areas with fragile, low quality lands. The Prairies of western Canada are blessed with a huge area of arable soils mostly of good quality. Similar to the world, all soils require good management to remain productive over the long term. Ten million hectares of Prairie soils are considered at risk in terms of both environmental and economic sustainability and require a continuing conservation effort and improved fertilizer management to remain productive.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.145 | 0.041 |
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".