Studying the interaction of crop management practices and weather and the subsequent effect on nitrous oxide emissions, 2000-2005 [Canada]: Instrumentation and Plot History
Bibliographic record
Abstract
This project involves the measurement of nitrous oxide fluxes from agricultural sources focussing on the interaction of crop management practices and weather and the subsequent effect on nitrous oxide emissions. The objective of this study were (1) to evaluate the magnitude of nitrous oxide emissions reduction due to best management practices in comparison to conventional management practices and (2) to study the seasonal variability in reduction of emissions due to the interaction between management and weather. This dataset is part of a long-term assessment measuring nitrous oxide emissions over a five year period (2000-2005). The data was collected within four experimental plots located at the Elora Research Station in Southern Ontario, Canada. Experimental plots consisted of two management systems: conventional practice (plots 1 and 4) and best management practice (plots 2 and 3). Data collected consists of meteorological data including vector data, crop height, crop yield measurements, plant matter and soil mineral nitrogen accumulations, nitrous oxide flux, soil bulk density, soil moisture content, soil temperature, as well as auxillary data including plot description/history information and instrument placement (i.e. placement and height).This study includes only information regarding experimental plot history and instrumentation including placement and measurements.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".