Second-year decomposition and nutrient release characteristics of ten annual crop residues in south-central Saskatchewan, Canada
Bibliographic record
Abstract
MATERIALS & METHODS• Post-harvest residues were collected in the fall of 2015 from farm fields in Saskatchewan, dried to a constant weight, and a 5-g subsample placed in a polyethylene screen bag (20 × 20 cm; 1 mm mesh) and stapled closed.Additional subsamples of the original residues (i.e., time = 0) were analysed for their nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) contents.• Prior to snowfall, the litter bags (n = 4) were placed on the soil surface of a farm field in south-central Saskatchewan and arranged in a completely randomized design.• The soil is an Orthic Brown Chernozem with pH 7.6 and 20 mg/kg extractable NO 3 -N, 10 P, 300 K, and 25 S in the 0-15 cm depth.• Four collection times were chosen: the spring of 2016 (six months) and the fall of 2016 (1 year), 2017 (2 years; reported here), and 2018 (3 years).• The residual litter was dried to a constant weight, weighed to determine mass loss, and analysed for its N, P, K, and S contents.• Meteorological data (air temperature, rainfall, relative humidity, wind speed, and snow depth), along with soil temp/moisture (0-60 cm), are being collected using an adjacent MET station. OBJECTIVE• Quantify the mass loss and changes in nutrient content of decomposing harvest residues from a variety of annual cereal, pulse, and oilseed crops grown in Saskatchewan: barley, wheat, oats, field pea, lentil, soybean, faba bean, canola, flax, and hemp.• Post-harvest plant residues represent a significant addition of carbon (C) and nutrients to soil; however, limited work has been done to investigate their fate within low disturbance agricultural systems, where these residues are not incorporated into the soil.• Quantifying these dynamics will improve our understanding of how different crop residues impact C sequestration and nutrient cycling, along with providing data for the development and validation of agroecosystem C and nutrient biogeochemical cycling models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".