RGIK guidelines for compiling consistent rock glacier inventories
Bibliographic record
Abstract
Rock glaciers are characteristic and ubiquitous periglacial landforms. They contain key information for understanding the past and present evolution of the mountain cryosphere, as well as for addressing a range of more applied concerns such as water supply/quality and geohazard assessment, especially in relation to ongoing climate change. Their spatial distribution and characterization, including their state of activity, has long been documented by means of rock glacier inventories (RoGIs). However, owing to the inherent morphological complexity of these landforms, contrasting definitions, and limited international cooperation, most RoGIs compiled around the globe exhibit a high degree of heterogeneity. This is a critical shortcoming that hampers our ability to combine RoGIs across regions towards the compilation of a global inventory. To address this limitation, the International Permafrost Association (IPA) Action Group (2018–2023) on Rock Glacier Inventories and Kinematics (RGIK) has fostered and coordinated international collaborative work to develop widely accepted guidelines for inventorying rock glaciers, including the characterization of kinematic behavior (RGIK, 2023a). Accordingly, a technical definition of rock glaciers and a methodological workflow for inventorying these landforms are provided. This RGIK definition relies on three morphological criteria: the mandatory evidence of a rock glacier front and adjoining lateral margins, and optionally, ridge-and-furrow topography. Deliberately, the definition does not address the questions of formative mechanism(s) and ice origin. To account for landform complexity, a hierarchical classification scheme of rock glacier units (RGUs) and systems (RGSs) is also introduced. The methodological workflow is composed of four steps: (i) detection, which consists of rock glacier identification according to the relevant morphological criteria; (ii) location, which involves assigning a georeferenced primary marker to each RGU and RGS; (iii) characterization, which among a set of optional attributes, entails assigning a geomorphological type of upslope connection and a degree of activity to each RGU; and (iv) delineation, in which the rock glacier outline is mapped and relevant degree of uncertainty is documented. Primarily, this workflow is based on a geomorphological approach, which may be supported with a kinematic approach, when reliable kinematic data is available. The coordination of ongoing testing, training, and prospective developments is entrusted to the IPA Standing Committee on RGIK, which was established in 2024.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.034 |
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".