Response of annual canarygrass (<i>Phalaris canariensis</i> L.) to nitrogen fertilizer and fungicide applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Annual canarygrass (Phalaris canariensis L.) is a specialty crop grown in Canada and the harvested grain is primarily used to feed wild and domesticated bird species. A field experiment was conducted at 5 locations in both 2012 and 2013 to study the response of annual canarygrass development and grain yield to the combined effects of fungicide (propiconazole + trifloxystrobin) and nitrogen (N) fertilizer, and to determine the minimum number of site years required to detect these effects. The experimental design was a split plot with fungicide application (none or fungicide) as the main plot and N application rate as the sub plot (10, 20, 30, 50, 70, 90 kg N·ha −1 ). There was a linear increase of 14.5% in grain yield as the rate of N fertilizer increased. The fungicide application increased the grain yield 8.5% by increasing kernel density from 15 197 kernels m −2 to 16 288 kernels m −2 . There was no interaction between the N rate and fungicide application. The application of a fungicide did not increase the responsiveness of annual canarygrass to N fertilizer. The lack of an interaction between N and fungicide application indicates that these two practices can be managed independently by annual canarygrass producers. To optimize grain yield producers should apply 50 kg N·ha −1 and apply a fungicide to increase grain yield especially in regions where septoria leaf mottle is prevalent. The number of site years needed to consistently detect the impact of N and fungicide on the grain yield were 4 and 5 site years, respectively.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it