Discussion on the sea–land telluric current vector and its continuity during a geomagnetic storm based on coastal stations in China and Canada
Bibliographic record
Abstract
The sea-land telluric current vector and its continuity during two geomagnetic storms are discussed using observation data of 13 geoelectric field stations within 100 km of the coastline in China and 5 similar stations in Canada. The results show that the amplitude of the geoelectric field varies up to 300–2600 mV/km at high latitudes and in the range 100–300 mV/km at low latitudes, below 100 mV/km at the middle latitudes, when the two geoelectric storms loading. The energy spectra of geoelectric field at RES in Canada is found to be concentrated in 16–48 min, and at CHL in China concentrated in 64–128 min. The telluric current flows directly to sea from the coastal land, except the two land-type stations with particular electrical structures. The sea-land current continuity model which was set up based on geological and geophysical data to deduce vertical circulation of current channels in continental and marginal seas area, which can explain the different current orientations in different regions. Our detailed analysis show that the direction of telluric current of Island stations is related to the deposition and river erosion and controlled by ocean currents during monsoon, are also included by the model. At last, the sea-land telluric current continuity model provides well understanding for the constraints of conductivity on the sea-land interface.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".