Orchid abundance in hemiboreal forests: stand-scale effects of clear-cutting, green-tree retention, and artificial drainage
Bibliographic record
Abstract
The current knowledge on the impact of forest management on plant species of conservation concern is poor. We asked how three basic silvicultural techniques (clear-cutting, green-tree retention, and artificial drainage) affect the abundance of terrestrial orchid species and their communities in Estonia, hemiboreal Europe. Fixed-area, fixed-effort surveys (4 h per 2 ha plot) were used in 29 plot clusters representing five site types, with each cluster including plots of four treatments (old growth, mature managed forest, and cutover with and without live retention trees). Altogether 11 species of orchids were recorded in those 116 plots, with the most complete sets of species in artificially drained plots and mature stands. Five species were widely distributed among treatments and site types, but most site types also hosted shade-tolerant orchids (six species) that characteristically disappeared after timber harvesting. Cutover areas (3–7 years after harvest) hosted no species absent from uncut forest stands, and retention of solitary trees had no effect on orchid abundance over clear-cuts. Modern Estonian forest landscapes appear to support viable populations of many orchids, with rotation ages sufficient for population development of the majority of shade-tolerant species.
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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 | 0.001 |
| 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".