A triumvirate of peat landslides: A transdisciplinary approach for a not-so-natural- hazard in Ireland.
Bibliographic record
Abstract
Landslides are traditionally viewed through geomorphological, ecological, geophysical, and hydrological lenses, often taking a multi-disciplinary approach. More recently, there have been calls for a different approach- one that acknowledges the increasing importance of socio-environmental stresses that can also affect slope instability. For Ireland, environmental stresses in peatlands are connected to the utilisation of land as a resource that is linked to commercia development, and indeed, towards supporting financial investment in rural areas by various governments, e.g., peat mining, agricultural grazing, afforestation, and wind farm construction. Weather conditions in Ireland during the 2020 summer and autumn were unusually dry and warm, followed by intense summer storms and longer duration rainfalls. These antecedent conditions are known to increase susceptibility to landslides, especially in organic (peat) soils. Three slope failures in peat occurred in 2020: one in June in Co. Leitrim and two in November in Co. Kerry and Co. Donegal. Landslide susceptibility ratings for all sites were in the lower range. We adopt a transdisciplinary approach in a study of these three peat landslides and bring together the disciplinary expertise of engineering, geomorphology, hydrology, ecology and land-use. These three sites provide an opportunity to better constrain the physical properties of peat for comparison with other ecologically similar sites, an area of research that is still developing. In addition, we focus on understanding the altered drainage (hydrology) and the recovery (through natural revegetation and succession) of these sites. We call for the assessment of the hazard to more fully include the feedback that exists through the connectivity of landscape systems (e.g., colluvial, fluvial and lacustrine/marine). In addition, we recommend that peat landslides be evaluated over extended timescales, beyond the immediate impacts to capture longer-term (secondary) effects on water and ecology in general as well as human enterprise. An inclusion of an analysis of the historical and modern land-use and management of peatland areas in the uplands of Ireland brings the study towards a fuller appreciation of the factors that increase peatland hazard. Our transdisciplinary approach ensures that the ‘real’ hazard types and levels are identified, particularly in the context of an increasing population expansion, changes in land use and trends in the climate scenario for Ireland.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".