Influence of planktonic foodweb structure on a system's capacity to support pelagic production: an inverse analysis approach
Bibliographic record
Abstract
Abstract Marquis, E., Niquil, N., Vézina, A. F., Petitgas, P., and Dupuy, C. 2011. Influence of planktonic foodweb structure on a system's capacity to support pelagic production: an inverse analysis approach. – ICES Journal of Marine Science, 68: 803–812. Coupled plankton/small pelagic (SP) fish systems were analysed to assess how foodweb structure influences the export of carbon to pelagic fish during the spring bloom in the Bay of Biscay. The investigation of carbon export flows through inverse analysis was supplemented by estimating the carrying capacity for pelagic fish production by applying linear programming. A planktonic foodweb dominated by microbial pathways had the highest trophic efficiency owing to the tight coupling between planktonic trophic levels and predation pressure on mesozooplankton by fish. Moreover, the magnitude of the gap between carrying capacity and estimated carbon export was related to the size structure of primary producers, with the picophytoplankton-based foodweb having the smallest gap and the microphytoplankton-based one the largest gap. Planktonic foodwebs dominated by small autotrophic cells channelled most of their available carbon to pelagic fish production, whereas foodwebs dominated by large phytoplankton were better suited to benthic communities with a large loss of carbon through sedimentation. Although the total carbon available to higher trophic levels does not vary with the size of the main primary producers, the potential export to SP fish depends on the structure of the planktonic foodweb.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".