ECOSSE biogeochemical modelling of soil organic carbon from Irish grassland systems - challenges and opportunities
Bibliographic record
Abstract
In this study we used the biogeochemical model ECOSSE-6.2b [1] in site-specific mode to evaluate/test model accuracy to estimate soil organic carbon (SOC) in Irish grassland systems under mineral soils. The selection of sites and management practices, as well as model inputs and model initialization followed procedures explained in Premrov et al. (2021) and (2020) [2],[3]. Results indicated a possible overestimation of modelled SOC for some grassland management categories, highlighted the sensitivity of the model to the initial SOC inputs and demonstrated the need for replicated measurements of SOC over time [4]. One of the challenges faced in this study was the lack of availability of site-specific data for the selected Irish sites, such as data on livestock stocking rates (SR) for grazed grasslands, which can differ greatly from year to year. SR could be only estimated as a single numeric value for each site, which demonstrated the need for greater availability and more detailed site-specific data for Irish grasslands. The availability of repeated measurements of SOC over time for the whole country represented another major challenge in modelling SOC for Irish grassland systems [4]. It is thought that the modelling undertaken here could be further enhanced using additional time-dependent SOC soil-point data, such as LUCAS data [5], as this would provide datasets that have repeated measurements of SOC needed for further model evaluation and parameterization. This work also showed a significant potential for further model improvement; grazing-induced vegetation changes, and associated impacts on SOC, could be accounted by introducing new types of grazed grassland vegetation parameters into the ECOSSE model [4]. These modelling opportunities could also have significant potential for further assessment of SOC dynamics and for spatial and temporal upscaling. Acknowledgements SOLUM project is funded under the Irish EPA Research programme 2014-2020. Literature [1] Smith, J., et al. (2010). ECOSSE. User Manual. [2] Premrov, A., et al. (2021). Insights into ECOSSE modelling of soil organic carbon at site scale from Irish grassland sites and a French grazed experimental plot. EGU21-1879. https://doi.org/10.5194/egusphere-egu21-1879; (CC BY 4). [3] Premrov, A., et al. (2020). Insights into modelling of soil organic carbon from Irish grassland sites using ECOSSE model. EGU2020-8090. doi.org/10.5194/egusphere-egu2020-18940; (CC BY 4). [4] Saunders, M. et al. (2021) Soil Organic Carbon and Land Use Mapping (SOLUM) (2016-CCRP-MS.40). EPA Research Report. [5] JRC (2020). LUCAS 2015, ESDAC. JRC. EC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".