Data used for the simulation of six experiments on N2O emissions from arable crops and simulation results of three agroecosystem models (MONICA, SiriusQuality and STICS)
Bibliographic record
Abstract
The dataset contains data from six arable crop field experiments, that provided high-quality data on N2O emission for simulation woth agroecosystem models. The experimental sites were located in Ottawa, Canada, Grignon, France, New Delhi, India, Kingaroy, Australia, Santa Maria, Brazil, and Estrées-Mons, France. The dataset contains two growing seasons of ACBrio cultivar (spring wheat) in Ottawa, two growing seasons of Premio cultivar (winter wheat) in Grignon, three growing seasons of PBW343 cultivar (spring wheat) in New Delhi, one growing season of Hartog cultivar (winter wheat) in Kingaroy, one growing season of Quartzo cultivar (winter wheat) in Santa Maria, and four growing seasons two spring barley cultivar (Sebastian and RGT Planet) and two winter wheat cultivar (Cellule and Absalon)) in Estrées-Mons. The data set contains detailed information on crop management, weather conditions, soil properties, daily N2O emission, and measurements over the growing season of soil , and water content. It also contains in-season measurements of leaf area index, total above ground biomass and nitrogen and final grain yield and nitrogen. For Ottawa, Grignon, New Delhi, Kingaroy and Santa Maria, the data set contains one treatment per year. For Estrées-Mons, data were available for five treatments in 2013 and 2014 and 6 treatments in 2017 and 2019. Each of these treatments is characterized by a combination of nitrogen fertilization, crop residue management, tillage depth, and cover crop. Simulations include both daily in-season and end-of-season results from 3 wheat crop models: MONICA, SiriusQuality and STICS.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".