Ventilation of the Arctic Ocean and the Gulf of St. Lawrence
Bibliographic record
Abstract
Ocean ventilation describes the physical process that transports surface waters into the ocean’s interior. The process thereby links surface waters with deep waters, contributing significantly to the global ocean conveyer belt and affecting global water mass circulation. This thesis analyzed transient tracer measurements, primarily focusing on the tracers CFC-12 and SF6, to examine ocean ventilation patterns. The investigation of ventilation patterns focused on two regions. First, the Arctic Ocean and the primary aims here were (1) to analyze ventilation patterns and their temporal variability, and (2) to evaluate the use of the ‘Medusa’-tracers to improve the ventilation analysis. Second, the Gulf of St. Lawrence, where the main objective was to analyze deep water ventilation and hereby predict changes in the composition of the water mass flowing into the Gulf. The examination of Arctic Ocean ventilation patterns at intermediate depths (250 – 1500 m) over the past three decades (1991 - 2021) revealed a significant temporal variability. The analysis identified a multidecadal variability in ventilation throughout this 30-year time period, showing reduced ventilation (higher mean ages) in 1991 and 2021 in comparison to 2005 and 2015. Additionally, it was observed that during the past 16 years (2005 until 2021) the ventilation slowed down on the Eurasian side of the Arctic Ocean, with increasing mean ages observed from 2005 over 2015 to 2021. Transient tracer measurements, were also utilized to analyze deep water ventilation in the Gulf of St. Lawrence. The data identified the presence of older water in the eastern part compared to the western edge of the Gulf, contrary to the expected flow pattern of the water. This underscored the increasing influence of older, less oxygenated NACW in recent times, which limits the influence of recently ventilated, oxygenated water coming from the Labrador Sea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".