Trends of satellite-derived thermal fronts and frontal chlorophyll concentration in marine hotspots: between 2003 and 2020
Bibliographic record
Abstract
Since the mid-20th century, humans have had an unprecedented impact on Earth’s climate. Carbon dioxide emissions and other greenhouse gas have driven global warming, leading to rising ocean temperatures. Oceanic frontal activity is expected to have changed significantly due to climate change and warming oceans. Such changes could strongly impact local ocean biogeochemistry and marine ecosystems in marine hotspots, regions with the most rapid warming, causing changes and redistribution of biomass and impacting higher trophic levels. A better understanding of these changing fronts today will help us predict and manage future responses of such regions to ocean warming. We use MODIS Aqua satellite-derived products: sea surface temperature (SST) and chlorophyll-a concentration; and a histogram-based frontal detection algorithm to derive 18-year time series (2003-2020) of the spatial density, annual probability, mean strength (SST gradient), and mean chlorophyll concentration of oceanic fronts. This analysis is applied in nine global marine hotspots: South East Australia, South West Australia, South California-Mexico, South-Brazil Uruguay, South East Canada-a/b, Galapagos, South Africa, and South Indian Ocean regions. Our results show frontal density has decreased significantly (fewer fronts) in six of the nine hotspots from 2003 to 2020. Frontal probability shows a decreasing likelihood for fronts to occur in five in nine hotspots. However, the mean frontal strength has been increasing in three of the nine hotspot regions, and frontal chlorophyll has increased in five hotspot regions. Based on our current results, the preliminary conclusions are that fronts are becoming fewer but stronger and with higher chlorophyll concentrations in marine hotspots. We are still working on covering more hotspots to check how widespread this is.
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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 | 0.001 |
| 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) | 0.001 | 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".