Scenario characterisation within a multi-factorial analysis of climate change impacts on whole farm systems. VIII European Society for Agronomy Congress
Bibliographic record
Abstract
Holistic studies of climate change (CC) impacts on whole farm systems require a range of assessment metrics to characterise the change scenarios. Characterisation of the change scenarios is required to enable results from an overall holistic study to be put into context, aiding interpretation of output, which will then permit potential adaptation and amelioration strategies to be identified and developed. This study details the use of several metrics, as the first part of a comprehensive holistic study to investigate and quantify the additional risk that climate change may have on the financial, social and environmental viability of two different farming systems. Materials and methods Sites and weather Climate change data were produced by two Global Circulation Models (GCM): Canadian (C); and Hadley (H), representing projected conditions at 2030 and 2090 for farms in two locations. One is an upland farm in Scotland, UK (Hartwood), characterised by cold wet winters and cool moist summers, with a combined sheep and suckler cow grazing system. The other is in Tuscany, Italy (Montepulciano), with cool moist winters and warm dry summers, with an integrated cropping and indoor reared beef system. Climate metrics Current and changed climates, applied to both sites over a 50-year period, were characterised by a set of assessment metrics chosen in order to capture projected changes in those conditions regarded to be of importance to soil accessibility and land use productivity. Summary statistics (mean, median) and exceedence probabilities (Pe) were applied to basic weather variables such as rainfall (R), air temperature (T) and evapotranspiration (ET0). Additional metrics were derived from the basic data (AP: access period (days); SMDm: maximum summer soil moisture deficit (mm); ADS: air-dried soil (days); RFC: return to field capacity (date);
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".