Bibliographic record
Abstract
We discuss some of the technical problerns in carrying out marineseismic multichannel measurements in ice covered oceans.A streamer noise analysis was carried out during the international expedition ARCTIC' 9 I in order to derive constraints for seismic measurements in such an environment.These data have been collected under the most severe operational conditions far seismic equipment.The noise analysis as weil as the processed data demonstrate that high quality seismic data can be collected in polar regions.Optimum conditions for continous seismic profiling in most severe ice conditions requires a second ice breaker in front of the seismic ship, which itsclf needs 10 be an ice breaker.Zusammenfassung: Technische Probleme, die sich bei der Durchführung von marinen, seismischen Messungen (Mehrkanal) in eisbedeckten Meeresgebieten ergeben, werden diskutiert.Eine Streamer-Noise Analyse, die während der internationalen Expedition ARCTIC' 9 I durchgeführt wurde, liefert Rahmenbedingungen für seismische Messungen in diesen Meeresgebieten.Diese Daten wurden unter den bisher schwierigsten Bedingungen für das geschleppte seismische Gerät gewonnen.Sowohl die Noise-Analyse als auch die verarbeiteten Daten zeigen,~daß es möglich ist, qualitativ hochwertige seismische Daten in den eisbedeckten Polargebieten zu sammeln.FÜreine kontinuierliche Meßfahrt unter schwierigsten Eisbedingungen ist allerdings ein zweiter Eisbrecher notwendig, der dem "Seismik-Schiff' (ebenfalls ein Eisbrecher) vorausfährt.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.988 | 0.988 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".