Breakdown and invertebrate colonization of dead wood in wetland, upland, and river habitats
Bibliographic record
Abstract
Breakdown of woody debris in river and upland habitats as well as the interactions between wood and invertebrates have been well described. Studies of wood in wetlands are rare, and far less is known about breakdown and invertebrate use of wood in these transitional habitats. This study experimentally assessed breakdown and invertebrate colonization of wood in a floodplain wetland and directly related patterns in the wetland to adjacent river and upland habitats. Over a 2.7 year period, we monitored breakdown and invertebrate presence in 10 cm diameter × 150 cm long sweetgum ( Liquidambar styraciflua L.) logs in a floodplain wetland (n = 8), river (n = 5), and upland (n = 4) habitat. Mass loss, decay condition change, and C/N ratios of wetland wood more closely resembled upland than river wood. The overall invertebrate assemblage associated with wetland wood was also more similar to that associated with upland than river wood. Breakdown and invertebrate colonization of wood in the floodplain wetland shared more characteristics with upland than river wood, perhaps because of the seasonal nature of flooding in the wetland. However, the ecology of wood in wetlands also had unique characteristics compared with either the uplands or the river.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".