Salvage logging effects on regulating and supporting ecosystem services — a systematic map
Bibliographic record
Abstract
Wildfires, insect outbreaks, and windstorms are increasingly common forest disturbances. Post-disturbance management often involves salvage logging, i.e., the felling and removal of the affected trees; however, this practice may represent an additional disturbance with effects on ecosystem processes and services. We developed a systematic map to provide an overview of the primary studies on this topic and created a database with information on the characteristics of the retrieved publications, including information on stands, disturbance, intervention, measured outcomes, and study design. Of 4341 retrieved publications, 90 were retained in the systematic map. These publications represented 49 studies, predominantly from North America and Europe. Salvage logging after wildfire was addressed more frequently than after insect outbreaks or windstorms. Most studies addressed logging after a single disturbance event, and replication of salvaged stands rarely exceeded 10. The most frequent response variables were tree regeneration, ground cover, and deadwood characteristics. This document aims to help managers find the most relevant primary studies on the ecological effects of salvage logging. It also aims to identify and discuss clusters and gaps in the body of evidence, relevant for scientists who aim to synthesize previous work or identify questions for future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.065 | 0.057 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".