Old growth forests and large old trees as critical organisms connecting ecosystems and human health. A review
Bibliographic record
Abstract
Abstract Old forests containing ancient trees are essential ecosystems for life on earth. Mechanisms that happen both deep in the root systems and in the highest canopies ensure the viability of our planet. Old forests fix large quantities of atmospheric CO2, produce oxygen, create micro-climates and irreplaceable habitats, in sharp contrast to young forests and monoculture forests. The current intense logging activities induce rapid, adverse effects on our ecosystems and climate. Here we review large old trees with a focus on ecosystem preservation, climate issues, and therapeutic potential. We found that old forests continue to sequester carbon and fix nitrogen. Old trees control below-ground conditions that are essential for tree regeneration. Old forests create micro-climates that slow global warming and are irreplaceable habitats for many endangered species. Old trees produce phytochemicals with many biomedical properties. Old trees also host particular fungi with untapped medicinal potential, including the Agarikon,Fomitopsis officinalis, which is currently being tested against the coronavirus disease 2019 (COVID-19). Large old trees are an important part of our combined cultural heritage, providing people with aesthetic, symbolic, religious, and historical cues. Bringing their numerous environmental, oceanic, ecological, therapeutic, and socio-cultural benefits to the fore, and learning to appreciate old trees in a holistic manner could contribute to halting the worldwide decline of old-growth 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".