Development of a farm-level greenhouse gas assessment: Identification of knowledge gaps and development of a science plan
Bibliographic record
Abstract
An Alberta?wide greenhouse gas (GHG) forum was held in March 2000, during which consensus was reached by representatives of the agriculture industry, the scientific community, and government that comprehensive on?farm GHG emission assessments were necessary if agricultural producers and processors were to reduce their GHG emissions – they have to know where and how much GHG they are emitting before they can reduce them. Before the possibility of on?farm GHG assessments can be conducted, a review of the scientific literature is required. In 2001, an in depth review of the scientific literature was initiated to gather, evaluate and synthesize agricultural GHG research for the Prairie region. The first chapter of the report summarizes the state of knowledge of agricultural GHG research and identifies preliminary gaps in our knowledge. This chapter was peer reviewed by scientific experts across Canada who were brought together in a workshop format to discuss their findings. The workshop participants prioritized the gaps with respect to urgency and impact. The identification of knowledge gaps helped lay the foundation for the Agricultural GHG Science Plan (chapter 3), which prioritized research in the areas of soils and crops, livestock, land use and energy and whole farm systems. In addition, an Alberta?based Agricultural GHG Inventory was updated (chapter 2) for 2001. All three sections of this report clearly identify agricultural GHG research gaps and recommend there is currently not enough information available to produce on? farm assessments that will accurately reflect the GHG emissions of a typical farm within a reasonable range of error.
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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 | 0.092 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".