Lichens on down wood in logged and unlogged forest stands
Bibliographic record
Abstract
Lichen communities of forests often appear to be negatively affected by timber harvest presumably because of reduction of suitable substrate and increased desiccation. We examined species richness and composition of lichens on wood of logs of the same decay class in unlogged stands (>140 years old) and logged, 20- to 30-year-old stands. There was no significant difference in species richness or mean lichen cover between logged and unlogged stands, but species composition differed, including species that were unique to either logged or unlogged stands. Crustose lichens accounted for 71% of rare species and all of the species occurring more commonly in unlogged stands; macrolichens accounted for 82% of common species and 60% of the species occurring more commonly in logged stands. Diameter at breast height and decay characteristics of down wood were the best predictors of lichen richness. Among lichen-rich, decay class 3 logs, relatively small amounts of retained down wood sustained lichen richness equivalent to unlogged stands. It appears important to ensure that decay classes favourable to lichens are retained after harvest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".