Data for Fertilizer Management and Environmental Factors Drive N2O and NO3 Losses in Corn: A Meta-Analysis
Bibliographic record
Abstract
lt;pgt;The specific aim of this meta‐analysis project was to determine the impact of 4R N management techniques on nitrous oxide (Nlt;subgt;2lt;/subgt;O) and nitrate (NOlt;subgt;3lt;/subgt;) losses relative to corn yield. The team collected and synthesized field research data published prior to July 2014 that measured N losses as affected by 4R fertilizer N management (right rate, source, timing, and placement) in North American corn‐based cropping systems.lt;/pgt; lt;pgt;Core ideas from the paper include: 1) Systematic review and meta-analysis demonstrate key factors for reducing agricultural N losses. 2) Nitrification inhibitors and side-dress fertilizer N each reduce Nlt;subgt;2lt;/subgt;O losses by ~30%. 3) Temperature controls Nlt;subgt;2lt;/subgt;O emissions and precipitation controls NOlt;subgt;3lt;/subgt; leaching losses. 4) Higher levels of soil carbon reduce NOlt;subgt;3lt;/subgt; losses, but increase Nlt;subgt;2lt;/subgt;O emissions. 5) Lack of simultaneous data for Nlt;subgt;2lt;/subgt;O and NOlt;subgt;3lt;/subgt; impedes understanding of tradeoffs and synergies.lt;/pgt; lt;pgt;This data publication includes all observations (management, crop response, and N loss details; for each treatment-site-year) used in meta-analysis models. The dataset contains 789 observations (417 with Nlt;subgt;2lt;/subgt;O losses and 388 with NOlt;subgt;3lt;/subgt; losses) with up to 72 variables each.lt;/pgt; lt;pgt;The Data Dictionary describes all variables. Because parts of the dataset have been updated/corrected since publication of the journal article, the Data Dictionary also includes notesamp;nbsp;that detail specific dataset modifications needed to reset a small number of variables to the status used in the meta-analysis models (in order to reproduce the exact regression models from the publication).lt;/pgt;
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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.050 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".