How rising ocean temperatures are influencing our weather
Bibliographic record
Abstract
Globally, surface air and ocean temperatures have warmed by about 1°C since 1900. More than 90% of the additional heat contained in the climate system (atmosphere, ocean, land) due to global warming is stored in the ocean, so what do these increased ocean temperatures mean for our weather? \n \nThe ocean stores significantly more heat than the atmosphere: the top few metres of the ocean contain more thermal energy than the entire atmosphere. There is a continuous exchange of heat between the ocean and atmosphere and the weather we experience is intrinsically linked to the heat contained in the ocean. \n \nAs atmospheric winds flow over the ocean, they typically pick up moisture and either gain or release heat. At mid-latitudes, and depending on the season, maritime air masses are usually either comparatively mild and humid (winter) or cool and humid (summer). Regions such as western Europe or the north-western US and western Canada experience maritime climates. These are characterized by reduced seasonal temperature extremes compared to locations at similar latitudes in the interior and along the east coasts of the continents as the prevalent westerly winds either come from the ocean (west coasts) or the interior of the continents (east coasts).
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".