Secondary production as a tool for better understanding of aquatic ecosystems
Bibliographic record
Abstract
A major challenge for ecologists is understanding ecosystem dynamics and function under environmental and anthropogenic stresses. An approach for addressing this challenge is the analysis of the different components contributing to secondary production (i.e., consumer incorporation of organic matter or energy per time unit) and how this production is influenced by external factors. Production studies have been recognized as a powerful tool in aquatic ecology, with applications in energy–biomass flow studies, trophic ecology, management of biological resources, as well as assessment of environmental stress. In this paper, we summarize ideas and techniques related to the estimation of secondary production and discuss how this approach may be used to evaluate ecological change in aquatic ecosystems. We include a critical review of classical methods and empirical models to estimate secondary production and provide several applications of production studies to current stresses affecting aquatic ecosystems, such as climate change, pollution, and the introduction of non-indigenous invasive species. Our goal is to illustrate the advantages of using secondary production as a more integrative tool for the assessment of the ecosystem function, in particular when subjected to strong anthropogenic and climatic stress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".