Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> The importance of monitoring the thermal state of permafrost is well recognized given the many hazards posed permafrost degradation under climate change. International collaborative efforts are in place to collate standardized permafrost monitoring data, aiming to establish baselines and early warning systems for potential consequences resulting from large-scale permafrost loss. Most of these data have been compiled from circumpolar regions and mountain environments in the Northern Hemisphere. To date, a scarcity of ground temperature monitoring data in South America has limited the understanding of the thermal state and possible degradation of mountain permafrost in Chile and Argentina. This study represents the first coordinated effort to compile and examine trends within ground temperature data from high altitude (i.e., 3,500 m to 5,500 m) mountain permafrost regions of the Central Andes. Ground temperature measurements were available from 53 boreholes installed by the private sector along a north-south transect at the Chilean-Argentine border (between 27° S and 34° S) within cold and warm permafrost, as well as non-permafrost zones. The dataset revealed similarities in ground temperature characteristics with those of other mountain permafrost regions, characteristics like high spatial and temporal variability, correlations with altitude and slope aspect, and distinct thermal attributes of rock glaciers. These observations suggest that processes shaping the thermal regime of the Central Andes are analogous to other mountain permafrost regions, which was previously hypothesized, but until now, not widely demonstrated with data. With the longest record in the dataset spanning 9 years, it is currently not possible to determine whether ground temperatures are increasing in response to climate warming. Instead, the high temporal variability observed in the data likely reflects short-term micro-climatic fluctuations and topo-climatic attributes unique to the Andean cryosphere, including hyper-arid conditions, intense solar radiation, lack of vegetative cover and organic matter, less massive ice (except for rock glaciers), and mountain topography in a southern hemisphere location. In addition, the susceptibility of the area to regional climatic phenomena (such as ENSO and PDO) implies that long-term trends may only be discernible from extended time-series datasets, spanning decades, highlighting the need for continuous and ongoing monitoring. A significant aspect of the presented dataset is its potential role in corroborating permafrost distribution models and upscaling efforts in the region, which currently lack in-situ data for calibration and validation. The study itself represents a unique and noteworthy collaboration between private industry, governments, and scientists, towards the advancement of understanding a key climate change indicator in a region with a paucity of ground-based data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".