Biotic and abiotic processes on granite weathering landforms in a cryotic environment, Northern Victoria Land, Antarctica
Bibliographic record
Abstract
Abstract A multidisciplinary study was carried out to understand the interactions between biotic and abiotic processes in granite weathering in ice‐free areas of Northern Victoria Land, Antarctica. Examples of tafoni, pits and grooves were analyzed, focusing on their morphometry, infills, weathering rind types and vegetation patterns. Surface and subsurface temperatures and incoming radiation were measured to characterize microclimatic conditions. In addition, microscopic, SEM and X‐ray diffraction analyses of granite were carried out. These analyses indicate that, under present conditions, mechanical weathering is the main process active in the formation of tafoni, which post‐date pits and grooves. In these forms, granular disintegration is mainly induced by chasmoendolithic lichens, salt and thermal stress associated with the dilatation coefficients of different granite‐forming minerals. The overall morphology of pits and grooves indicates that they originate from water erosion. In the former, mechanical weathering prevails, caused by epilithic lichens, by freeze–thaw events, and by salt, while only the first two processes are active in the grooves. The intensity of these processes is less effective than in tafoni and on the outer surfaces, suggesting that pits and grooves are inherited features, possibly generated in the same way as landforms occurring on granite in the humid tropics. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".