Replication data for: Reducing short-term cover crop expense in a corn-soybean-winter wheat rotation
Bibliographic record
Abstract
This dataset includes data from the Master's thesis "Reducing Short-Term Cover Crop Expense in a Corn-Soybean-Winter Wheat Rotation". The dataset includes three seasons of cover crop field data as well as the data from the subsequent corn season. Abstract: A common rotation in Ontario is the corn (Zea mays L.)- soybean (Glycine max L.)-winter wheat (Triticum aestivum L.) rotation. Using cover crops after winter wheat harvest could potentially benefit the rotation both environmentally and agronomically. This study helps determine whether the short-term cost of these cover crops can be negated by comparing various cover crop treatments and their potential to produce harvestable forage for additional income. Additionally, cover crop treatments, with and without being harvested, were compared for their potential to produce a fertilizer nitrogen replacement value (FNRV) savings to the subsequent corn crop. Lastly, cover crop management strategies such as a fall manure application and different planting arrangements in cover crop mixtures were compared as a means of improving forage yield and FNRV.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.032 |
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".