Deep Chlorophyll Maxima in the Global Ocean: Occurrences, Drivers and Characteristics
Bibliographic record
Abstract
Stratified oceanic systems are characterized by the presence of a so-called Deep Chlorophyll a Maximum (DCM) not detectable by ocean color satellites. A DCM can either be a phytoplankton (carbon) biomass maximum (Deep Biomass Maximum, DBM), or the consequence of photoacclimation processes (Deep photoAcclimation Maximum, DAM) resulting in the increase of chlorophyll a per phytoplankton carbon. Even though these DCM (further qualified as either DBMs or DAMs) have long been studied, no global-scale assessment has yet been undertaken and large knowledge gaps still remain in relation to the environmental drivers responsible for their formation and maintenance. In order to investigate their spatial and temporal variability in the open ocean, we use a global data set acquired by more than 500 Biogeochemical-Argo floats given that DCMs can be detected from the comparative vertical distribution of chlorophyll a concentrations and particulate backscattering coefficients. Our findings show that the seasonal dynamics of the DCMs are clearly region-dependent. High-latitude environments are characterized by a low occurrence of intense DBMs, restricted to summer. Meanwhile, oligotrophic regions host permanent DAMs, occasionally replaced by DBMs in summer, while subequatorial waters are characterized by permanent DBMs benefiting from favorable conditions in terms of both light and nutrients. Overall, the appearance and depth of DCMs are primarily driven by light attenuation in the upper layer. Our present assessment of DCM occurrence and of environmental conditions prevailing in their development lay the basis for a better understanding and quantification of their role in carbon budgets (primary production and export).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".