Synthetic Logs for Controlled Culture of Epixylic Bryophytes: Log Physical Properties, Surface Moisture, and<i>Dicranum flagellare</i>Growth
Bibliographic record
Abstract
Experimental studies of epixylic bryophytes require stable, biologically inactive substrata with physical properties that mimic those of coarse decayed wood. In two preliminary tests, we compared three synthetic logs (made from upholstery wadding, mattress foam, and floral foam) to decayed natural logs, in terms of their physical properties, moisture transmission, and ability to support growth of Dicranum flagellare in the laboratory. We also tested the effects of clumped vs. smeared propagule application on growth response. Vegetative moss propagules (flagellae and dried gametophore fragments) were applied as clumps or smears with nutrient agar gel, and evaluated as to horizontal expansion, vertical growth and dry weight of new growth over 6 months. Synthetic logs had higher porosity and lower density than natural logs, but showed similar moisture transmission capabilities to each other and to natural logs when moisture was supplied from the base. Nevertheless, surficial water potentials were consistently less than -5 MPa, and were therefore incapable of supporting bryophyte growth without applying liquid water from above. Horizontal expansion of D. flagellare was greatest with a smear application on floral foam logs, but vertical growth was greatest on upholstery wadding logs with a clumped application. Although floral foam (substratum) and smear (application) produced the greatest new shoot growth, observations suggest that the moss may allocate more growth to rhizoids on more penetrable substrata (those with a larger mean pore size). Future studies must modify the structure of synthetic substrate units to more closely mimic the moisture-related characteristics of rotting wood of target species, and further isolate the components of bryophyte growth.
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) | 0.001 | 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".