Quantifying cropping practices in relation to inoculum levels of <i>Fusarium graminearum</i> on crop stubble
Bibliographic record
Abstract
This study was conducted in 58 producer‐field locations in Manitoba from 2003 to 2006 to understand how cropping practices influence Fusarium graminearum inoculum levels on stubble of various crops, including wheat, collected from the soil surface. Colonies per m 2 (CN) were determined and converted to base‐10 logarithm values (log 10 CN). Mean log 10 CN of the sampled field for various crops and groups of crops grown in the 3 years prior to sampling were tested to find significant differences. Average log 10 CN values were also used to determine significant differences between tillage systems and the effect of number of years. Average log 10 CN values for zero and minimum tillage systems were not different but were significantly higher than values for conventional tillage. A series of three crop rotation scenarios were tested using weighted log 10 CN values for crop, tillage, their interaction and their squared terms in step‐wise regression models to identify which model was the best predictor of log 10 CN. This was selected as the cropping practice index (CPI) model and was expressed as: CPI = 1·98423 + 0·55975 ( C 2 × C 1 × T ) 2 + 0·4390 ( C 2 × T ) 2 , where C 1 , C 2 and T represent the weighted log 10 CN values for crops grown 1 and 2 years previously and tillage system, respectively. R 2 value for this model was 0·933 ( P < 0·0001). The reliability of the CPI model was tested using jack‐knife full cross‐validation regression. The resulting R 2 was 0·899. The CPI model was tested using data collected from seven wheat fields in 2006 ( R 2 = 0·567). The relationship between CPI and FHB index ( R 2 = 0·715) was significant.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".