Soil science education: A multinational look at current perspectives
Bibliographic record
Abstract
Abstract Soil knowledge is essential to address modern global challenges. Soil science education began with soil survey and agricultural activities, with a focus on the traditional subdisciplines of soil chemistry, soil physics, pedology, soil mineralogy, and soil biology. Soil education has evolved to address the needs of an increasing variety of fields and increasingly complex issues, as seen through the move to teach soil content in programs such as biological and ecological sciences, environmental science, and geosciences. A wide range of approaches have been used to teach soil topics in the modern classroom, including not only traditional lecture and laboratory techniques but also soil judging, online tools, computer graphics, animations, and game‐based learning, mobile apps, industry partners, open‐access materials, and flipped classrooms. The modern soil curriculum needs to acknowledge the multifunctionality of soils and provide a suite of conduits that connect its traditional subdisciplines with other cognate areas. One way to accomplish this may be to shift from the traditional subdiscipline‐based approach to soil science education to a soil functions approach. Strategies to engage the public include incorporating soil topics into primary and secondary school curricula, engaging the public through museums and citizen science projects, and explaining the significance of soil to humanity. Soil education has many challenges and opportunities in the years ahead.
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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".