Phytoplankton size structure, distribution, and primary production as the basis for trophic analysis of Caribbean ecosystems
Bibliographic record
Abstract
Abstract Forget, M-H., Platt, T., Sathyendranath, S., and Fanning, P. 2011. Phytoplankton size structure, distribution, and primary production as the basis for trophic analysis of Caribbean ecosystems. – ICES Journal of Marine Science, 68: 751–765. An oceanographic survey was conducted in Caribbean waters in April and May 2006 to assess the marine ecosystem in the context of a trophic analysis of the foodweb. Analyses of pigment and absorption data revealed that the phytoplankton community was dominated by pico- and nanophytoplankton, particularly at the deep chlorophyll maximum. Based on cluster analysis of remotely sensed data, three dynamic provinces were defined for the region. A 5-year time-series of sea surface temperature and chlorophyll concentration provided information on the annual cycle of these properties. To implement the computation of primary production on a synoptic scale, parameters characterizing the biomass profiles and photosynthesis–irradiance relationships were assigned using four protocols: two regional approaches, a regression with surface chlorophyll, and the nearest-neighbour method (NNM), which has the advantage of assigning parameters on a pixel-by-pixel basis. Monthly images of primary production were computed over an annual cycle using MODIS chlorophyll a concentration. The NNM and the use of dynamic provinces emerged as the methods of choice for parameter assignment. Finally, a new approach from remotely sensed data was developed to estimate production-to-carbon ratios, a key input to Ecopath models. The results contribute to ecotrophic analysis of the Lesser Antilles Pelagic Ecosystem project.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".