The relationship between bark substratum and conspecific biomass affecting hydration and drying rate of a lichen epiphyte (<i>Platismatia glauca</i> (L.) W.L. Culb. & C.F. Culb.)
Bibliographic record
Abstract
There is growing interest in the ecosystem roles contributed by “nonvascular photoautotrophs” (NVPs). This includes the biomass of epiphytic lichens and bryophytes, which can potentially absorb large amounts of atmospheric moisture (relative to their dry mass) therefore modifying hydrochemical pathways through the forest ecosystem. One further possibility is that NVP epiphytes can access moisture from, or at least have slower drying rates when in contact with, saturated bark surfaces. We tested this by comparing the drying rates for epiphytic lichen thalli positioned onto saturated bark of two types, with or without a conspecific biomass, relative to a free-drying control. We found little or no effect of saturated bark, but a strong effect of the conspecific biomass in slowing the thallus drying rate. The weak bark effect is possibly explained by the hydrophobic lower surface of the lichen species that was investigated ( Platismatia glauca (L.) W.L. Culb. & C.F. Culb.), which may act as a barrier to water uptake, and which may—contrary to our original hypothesis—slow bark surface drying. Although it seems highly plausible that bark–lichen hydrological interactions are important for incorporating NVPs into ecosystem models, the nature of these interactions may be more subtle than previously supposed, requiring further investigation.
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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 | 0.000 |
| 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".