The global potential of log-driven trees for reconstructing forest ecosystems dynamics
Bibliographic record
Abstract
The composition and structure of modern forest ecosystems result from past and present climate as well as centuries of anthropic and natural disturbances. Concerns related to the integrity and resilience of forests in the context of climate change have led to novel ecosystem-based management methods that require extensive knowledge about the preindustrial state of forests and past disturbance regimes. At the beginning of industrial forest exploitation, waterways were used as the main conduits to transport wood, but the timing and impacts of this log driving remain understudied. Given that an estimated 15% to 50% of log-driven logs sank during their transport, this accumulation of subfossil wood can serve as a proxy tool for reconstructing the dynamics and structure of preindustrial forests and inform modern forest management practices. This review provides a global overview of log driving and highlights the significant value of these submerged logs for disturbance ecology. We demonstrate that log driving was used on most continents, implying that proxy records from subfossil logs may be available from numerous boreal and mountainous regions. Our review is one of the first to illustrate the paleoecological value of log-driving remnants and explain how such a resource provides a valuable tool for understanding past forest ecosystems. Such knowledge is crucial for informing forest management in the face of climate change.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".