MACRO-INVERTEBRATE FUNCTIONAL GROUPS IN FRESHWATER AND MARINE SEDIMENTS: A COMMON MECHANISTIC CLASSIFICATION
Bibliographic record
Abstract
Functional group definitions in aquatic ecology vary depending on the type of ecosystem (e.g., marine compared to fresh water ecosystems, stream compared to lake ecosystems). Since the benthic environment functions as the major storage and recycling compartment for virtually all material that flows in the aquatic system, biological processes that take place there are interesting models for identification of the different invertebrate functions. The accurate species function includes the effects of an organism on the abiotic as well as the biotic properties of the habitats. Therefore, a functional group may be defined as a group of species that share common biogeochemical and interspecific attributes. The main difficulty of applying this definition in aquatic ecosystems comes from the high diversity of organism functions (compared to terrestrial systems) that potentially exist at different levels: i) interspecific diversity supported by the large morphological and behavioural diversity of organisms, reinforced by a low degree of species redundancy; ii) intraspecific functional diversity due to changes in life history strategy during the life cycle, and enhanced by optimal foraging theory. The major question remains to find accurate functional group sizes and classifications that permit the distinction of the different biological activities involved in ecosystem key processes without missing other biological functions. After reviewing some of the problems in current functional classifications of benthic invertebrates, we propose a classification system based on the mechanical activities that characterize each species rather than consideration of the multiple consequences of these activities. This sorting strategy will result in a sub-classification of classical feeding groups into more precise functional groups. Such groups as bioturbation groups or functional feeding groups may be composed of representative taxa in both marine and freshwater environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".