The results of an NPK-fertilisation trial of long-term crop rotation on carbonate-rich soil in Estonia
Bibliographic record
Abstract
Soil is an element of crop cultivation that demands consistent fertilisation to compensate for the nutrients that are removed by the harvest. Changes in soil because of prolonged fertilisation can only be estimated by long-term field trials. Experiments in long-term field trial site Kuusiku (since 1965) include crop rotation of potato, late harvest barley, early harvest barley undersown with forage grasses (red clover + timothy), 1-year forage grasses, 2-year forage grasses, and winter rye. Various combinations of mineral and organic fertilisers were used to investigate the yield, soil humus, phosphorus, and potassium content (available and total) of the top- and subsoil. Fertilisation improved the yield of different crops by 1.3–2.6 times; meteorological conditions caused the yield to vary up to 6.4 times. The concentration of humus decreased 0.2% when not using inorganic and organic fertilisers; use of fertilisers increased the concentration of humus by 0.2–0.6%. The humus-rich subsoil (3.5% humus) contained less available phosphorus than humus-poor subsoil (humus 3.0%), which had 29 and 63 mg PDL kg−1, respectively. Grasses in crop rotation enriched the soil with organic matter and reduced the excess of nutrients remaining from previous fertilisation, thereby decreasing nutrient leakage and eutrophication of bodies of water.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".