Time series of temperature in Fram Strait determined from the 2008–2009 DAMOCLES acoustic tomography measurements and an ocean model
Bibliographic record
Abstract
Abstract A pilot acoustic tomography program in Fram Strait during 2008–2009 measured a year‐long record of acoustic travel times along a 130 km range acoustic path crossing the West Spitsbergen Current. Individual ray arrivals were not observed. Rather, the arrival patterns consisted of a single, stable, broad arrival pulse of about 100 ms duration. Travel time variations of ±0.15 s recorded the vigorous mesoscale environment of the region and the seasonal cycle. To estimate ocean temperature from the tomography data an inverse scheme employed a high‐resolution ocean model for Fram Strait as the reference ocean. The information from the tomographic measurements is primarily average temperature. Estimated temperatures, averaged over 0–1000 m depth and over range, had a mean of 1.11°C and variations of ±0.33°C; the uncertainty of the tomography estimates was about 60m°C. Agreement with an alternate inverse approach based on EOFs and a Markov Chain Monte Carlo inversion scheme relying on a matched‐peak approach was excellent, indicating a robust estimate for ocean temperature. The inverse estimates for average temperature agreed with the equivalent estimates from hydrographic sections obtained along the acoustic path at the start and end of the program. Among other deficiencies, the ocean model greatly underestimated the intensity of the mesoscale fluctuations and exhibited a warm bias of about 0.38°C in section‐averaged temperature. Tomographic measurements in Fram Strait offer unique large‐scale temperature constraints for ocean models through data assimilation. It is anticipated that these constraints will lead to more accurate estimates of the circulation and transports in Fram Strait.
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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) | 0.000 | 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".