OXFORD • LONDON • EDINBURGH • NEW YORK TORONTO • PARIS • FRANKFURT SOME INTER-RELATIONSHIPS BETWEEN DECOMPOSITION OF VARIOUS PLANT RESIDUES AND LOSS OF SOIL ORGANIC MATTER AS MEASURED BY CARBON-14 LABELLING
Bibliographic record
Abstract
The addition of fresh plant material to soil has been reported to accelerate the decomposition of indig-enous soil organic matter. This claim has been tested at Beltsville, Maryland using C 14-labelled tissue of tops and roots from several crop plants of different maturity stages and C/N ratios. Two treatments were made on a Prairie soil from North Dakota containing 3. 5 per cent carbon. In one, the previously stored soil was incubated for two weeks before adding the plant material, and in the second, there was no preincubation. One per cent of C 14-labelled soybean, wheat or corn plant tops or roots was added to the soil and incubated in a closed system. The evolved CO, was absorbed in stand-ard NaOH solution that was sampled and titrated at intervals and the C 14 counted by liquid scintillation. In experiments where the soil was not preincubated, a slight increase in soil carbon loss was observed with the addition of mature corn leaves, 28-day-old soybean tops, mature soybean tops or roots. A re-duction in soil organic matter loss was found with the addition of young corn tops or roots, mature corn stalks or roots, wheat straw or roots (both young and mature), 28-day-old soybean roots, and 44-day-old soybean tops or roots. When the soil was preincubated before addition of the plant material all plant parts reduced soil
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.390 | 0.143 |
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".