A reflector at 200 km depth beneath the northwest Pacific
Bibliographic record
Abstract
We present an analysis of precursors to PP produced by underside reflections from discontinuities in the upper mantle beneath the NW Pacific. The events used for this study occur in the western Pacific Rim (New Zealand, Fiji, Tonga, Solomon, New Guinea, Philippine Islands) and are recorded at the short-period Yellowknife Array (YKA) in northern Canada. The source–receiver combination results in PP reflection points which allow us to study the upper mantle structure in a corridor from the Hawaiian Islands to the Kuril subduction zone. To detect the weak precursors in the time window between the P arrival and the PP onset and to identify them as PP underside reflections, special array techniques are used. Our analysis indicates a reflector at a depth of ∼200km beneath the northwestern Pacific. This reflector shows strong topography of some tens of kilometres on length scales of several hundred kilometres, complicating the detection of this reflector in global or regional stacks of seismograms. Different models for the impedance jump across the reflector, the thickness and the possible fine structure of the reflector are modelled using synthetic seismograms and are compared with the data. The thickness of the reflector has to be less than 7km and the P wave impedance contrast has to be larger than 5.0–6.5 per cent to be detected by this study. This corresponds to a P-velocity jump of ∼4 per cent assuming the PREM density model.
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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 | 0.000 | 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) | 0.001 | 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".