The absorption and evaporation of water vapor by epiphytes in an old‐growth Douglas‐fir forest during the seasonal summer dry season: Implications for the canopy energy budget
Bibliographic record
Abstract
Abstract Our goal was to determine how epiphytic lichens and bryophytes affect canopy latent heat fluxes in an old‐growth Douglas‐fir forest when the canopy was dry. The epiphyte water content (WCe expressed as a percent of dry weight) of representative epiphytic foliose lichens, fruticose lichens, and bryophytes was measured in the laboratory after 1 to 12 hr of exposure at five different values of vapor pressure deficit (VPD). After 12 hr of exposure, WCe increased fivefold to sixfold as VPD decreased from 1849 to 132 Pa. In addition, we measured WCe in the field using strain gauges. These field measurements were used to calibrate the models described below. Two models were created to estimate the potential latent heat flux from epiphytes at the canopy scale (LEe). The first model combined measured total biomass of epiphytes with a model that estimated the laboratory determined VPD‐dependent changes in WCe of the lichens/bryophytes (VPD method). The second model estimated LEe by scaling the change in WCe of epiphyte‐laden branches that were continuously monitored in situ in the canopy by a strain gauge (SG method). Both methods showed a strong diurnal trend in LEe when VPD was less than 645 Pa. Prior to sunrise, the epiphytes absorbed water, corresponding to a latent heat flux of 5 to 15 W/m2 per unit ground area, whereas after sunrise, the epiphytes lost water at a rate of −10 to −20 W/m2. For short periods, epiphytes may contribute a significant portion of the latent heat flux from Douglas‐fir forests.
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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) | 0.000 | 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".