LONG-TERM EFFECTS OF INTENSIVE CULTIVATION ON SOIL QUALITY IN THE POTATO-GROWING AREAS OF NEW BRUNSWICK (CANADA) AND MAINE (U.S.A.)
Bibliographic record
Abstract
The development of agricultural technology can cause both favorable and unfavorable changes in soil-plant environment. Our concern in this symposium is confined to the unfavorable changes caused by long-term intensive cultivation. Intensive cropping undoubtedly tends to deteriorate the physical condition of most soils. These soils ultimately become more difficult to manage and crop yields decrease. The problem. however. is not a new one, nor is the problem local. For example , Modern Farming and the Soil (Agricultural Advisory Council l97 I ) focused attention on deterioration of soil structure in the United Kingdom, a condition so vital to plant nutrition, from many modern farming techniques that employ large and heavy machinery. The dust-bowl conditions in the United States in the 1920's and 1930's, and also the widespread soil erosion that accompanied the continuous fallow-wheat system in Australia in the early part of this century, are grim warnings of situations that can develop if attention is not given to good soil management. But the soil condition that makes the situation in New Brunswick and Maine differ from those elsewhere is the presence of stones in the soils that farmers remove to facilitate mechanical harvestins. In this discussion, therefore, we will focis attention on soil compaction, erosion and stone removal and their interactions on crop yield under continuous potato cropping.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".