Bibliographic record
Abstract
The present concern for decreased tillage in crop production systems makes it interesting to look back 300 years to when Jethro Tull introduced his system of intensive tillage for cereals and fodder crops. His experiments in England, at a time when landowners started paying more attention to farming, established that frequent tillage with plows could substitute for manure or fallow in continuous cropping of wheat. The general understanding was that exposed soil absorbed plant nutrients from the atmosphere; therefore, it was important to turn over lower layers and to leave the soil in a loose condition where more surfaces were exposed. In addition to controlling weeds, a probably equally important factor was speeding up mineralization of organic matter through increased aeration. This would account for the increased yields even where weeds were not present, and the ability to substitute tillage for fallow. Tull's system of cultivating wheat grown in rows, sometimes on ridges, was vehemently debated in the 18th century and was not widely accepted. The benefits would have diminished as organic matter content of soils decreased, and was probably not effective on all soils. Key words: Horse-hoeing husbandry, mineralization, ploughing, Jethro Tull
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".