Reexamining the potential to classify lava flows from the fractality of their margins
Bibliographic record
Abstract
Can fractal analysis enable us to classify a lava flow to a morphologic type (e.g., ‘a‘ā vs. pāhoehoe) solely by examining the geometry of the flow’s margin? If so, these classifications would provide insights into the rheology and dynamics of the flow when it was emplaced. Furthermore, the potential to classify lava flows from remotely-sensed data would particularly benefit the analysis of flows for which field access is not feasible. The technique’s current framework depends on three assumptions: (1) measured lava margin fractality is scale-invariant, (2) different morphologic types are consistently distinguishable based on their measured fractality, and (3) any modification of margin fractality by substrate slope or topographic confinement would be minimal or have a recognizable signature. In this study, we critically evaluate each of these assumptions at meter scales using 15 field-collected margin intervals from a wide variety of morphologic types in Hawaiʻi, Iceland, and Idaho. Among the 12 margin intervals that satisfy the current framework’s expectations, 5 exhibit notably scale-dependent fractality and all 5 from transitional lava types would be classified as ‘a‘ā or pāhoehoe at some scales. Additionally, an ‘a‘ā flow on a 15° slope (Mauna Ulu, Hawaiʻi) and a spiny pāhoehoe flow confined by a stream bank (Holuhraun, Iceland) exhibit significantly depressed fractalities but lack distinctive signatures for these modifications. We therefore conclude that all three assumptions are invalid at meter scales. Although fractal analysis of lava margins can provide some constraints on morphologic type, unique classification is not robust at these scales.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".