Site factors determining epiphytic lichen distribution in a dieback-affected spruce-fir forest on Whiteface Mountain, New York: stemflow chemistry
Bibliographic record
Abstract
Epiphytic lichen diversity in a dieback-affected forest of red spruce (Picea rubens Sarg.) and balsam fir (Abies balsamea (L.) Mill.) on Whiteface Mountain, New York, U.S.A., was higher on dead compared with living trees and on fir compared with spruce. Diversity differed more between living and dead spruce than between living and dead fir. Cover of all lichen species that occurred on more than 50% of the sample trees, except for two species, decreased with increasing mean concentration of NO3in stemflow. Concentrations of NO3were higher on living spruce compared with dead spruce and with living and dead fir. The negative correlations between lichen cover and NO3concentration may reflect either a decrease of lichen abundance caused by toxic effects of higher NO3concentrations or a removal of NO3from stemflow by epiphytic lichens. Experimental exposure of Hypogymnia physodes to NaNO3reduced chlorophyll concentrations. This result, together with estimations of lichen and needle biomass, indicates that a dependence of lichen cover on NO3concentrations in stemflow may be the cause for the negative correlations. The sulphur concentration in stemflow did not affect lichen abundance on Whiteface Mountain. The manganese concentration in stemflow may have an effect on single species.Key words: forest dieback, manganese, nitrate assimilation, nitrate toxicity, precipitation chemistry, sulphur.
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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".