Hillslope erosional features and permafrost dynamics along infrastructure in the Arctic Foothills, Alaska
Bibliographic record
Abstract
Abstract Abrupt thaw of ice‐rich permafrost in the Arctic Foothills yielded to the formation of hillslope erosional features. In the infrastructure corridor, we observed thermal erosion and thaw slumping that self‐healed near an embankment. To advance our understanding of processes between infrastructure and hillslope erosional features (INF‐HEF), we combined climate and remote sensing analyses to field investigations to assess an INF‐HEF system and validate our findings in a broader area along the infrastructure corridor. We identified that thaw consolidation along an embankment formed a thermokarst ditch that was ubiquitous in the broader study area, and which was extensively affected by shrubification and supported other positive feedback (e.g., snow accumulation, water impoundment, and weakened vegetation mat). The thermokarst ditch facilitated channelization of cross‐drainage water, thus increasing the terrain vulnerability to thermal erosion that evolved into thaw slumping after heavy rainfalls. The terrain resilience to thaw slumping benefited from the type of ground ice and topography prevailing at our site. The lateral discontinuity of massive ice in an ice‐wedge polygonal system (i.e., interchange soil and massive ice) compounded to a low‐slope gradient with topographic obstacles (e.g., baydzherakhs) decreased slumping activity and supported self‐stabilization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".