Habitat preferences of an arboreal forage lichen in a Sierra Nevada old-growth mixed-conifer forest
Bibliographic record
Abstract
The arboreal forage lichen Bryoria fremontii (Tuck.) Brodo & D.Hawksw. appears sensitive to conditions of prolonged hydration in wet forests of British Columbia. I estimated the abundance of this lichen in mixed-conifer forest canopy in the contrasting Mediterranean climate of the southern Sierra Nevada in relationship to the vertical gradient of vapor pressure deficit. Abundance was estimated by biomass in 5 m strata from the ground to the tops of 50 trees. Transplants of Bryoria thalli were installed in 18 fir trees for 1 year to assess their growth relative to distance from perennial streams. VPD generally increased with height, being significantly greater at 45 m than 5 m. Bryoria biomass averaged across tree heights was estimated as 15.9, 0.60, 0.15, 0.25, and 0.19 g·m–1in red fir (Abies magnifica A.Murray), white fir (Abies concolor (Gordon & Glend.) Hildebr. var. lowiana (Gordon) Lemmon), incense cedar (Calocedrus decurrens (Torr.) Florin), Jeffrey pine (Pinus jeffreyi Balf.), and sugar pine (Pinus lambertiana Douglas), respectively. Transplant growth was significantly greater in trees <25 m from water (11.4%) than in trees >25 m from water (9.7%). Bryoria had strong positive associations with red fir, proximity to streams, and decreasing VPD. The sensitivity of Bryoria to prolonged hydration does not appear to be important in this climate characterized by extended summer dryness.
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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".