A reassessment of spectral<i>T</i><sub><i>e</i></sub>estimation in continental interiors: The case of North America
Bibliographic record
Abstract
Conventional spectralTestudies use the real part of the admittance between gravity anomalies and topography or, alternatively, the square of the magnitude of the coherency (i.e., coherence). Here we show the utility of treating both the admittance and coherency as complex quantities. Inverting the real parts to estimateTe, we use the imaginary parts to tell if the inversion is biased by noise. One method inverts the square of the real coherency, with the internal‐to‐total load ratioFderived (as a function of wave number) directly from the gravity and topography. The other method inverts the real part of the admittance assuming thatFis wave number‐independent. We test the methods using synthetic elastic plate models loaded at the surface and Moho in such a way that the final relief is the actual North American topography. In some of the models we add gravity noise generated by a model having both surface and internal loads such that the final topography is zero and find that both methods are susceptible to noise. Application of the two methods to North America givesTemaps showing substantial agreement except in regions affected by noise, but these are not a dominant part of the total area. Given the suggested mechanisms by which noise might arise, it is not surprising that it is not a more widespread feature of the North American craton. Importantly, both methods show that large parts of the Canadian Shield are characterized byTe> 100 km.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".