Decay processes in woody debris influence the taxonomic and functional composition of littoral macroinvertebrates
Bibliographic record
Abstract
We examined the effects of woody debris decay state on the densities, taxonomic and functional composition, and diversity of macroinvertebrates in littoral zones of undeveloped lakes. Our study revealed that the interacting effects of increasing roughness and softness of the wood, as well as changes in the quality of food resources with progressing decomposition of woody debris, exerted the strongest impacts on macroinvertebrate communities. Structurally complex, decomposed wood supported higher densities and species richness of macroinvertebrates than undecayed wood. We also found several taxa that were typical for decayed wood, including macroinvertebrates considered as xylophages. Decaying wood underpinned greater functional richness than undecayed wood, with high densities of collector–gatherers and shredders that could benefit from organic matter originating from decomposing wooden tissue, as well as predators attracted by numerous potential prey inhabiting this complex habitat. As decaying wood enhanced abundant and diverse macroinvertebrate communities, which in turn could subsidize upper trophic levels, it provides a valuable habitat in littoral zones, particularly in lakes with already sparse macrophyte cover.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".