Architecture of the wood‐wide web: <i>Rhizopogon</i> spp. genets link multiple Douglas‐fir cohorts
Bibliographic record
Abstract
*The role of mycorrhizal networks in forest dynamics is poorly understood because of the elusiveness of their spatial structure. We mapped the belowground distribution of the fungi Rhizopogon vesiculosus and Rhizopogon vinicolor and interior Douglas-fir trees (Pseudotsuga menziesii var. glauca) to determine the architecture of a mycorrhizal network in a multi-aged old-growth forest. *Rhizopogon spp. mycorrhizas were collected within a 30 x 30 m plot. Trees and fungal genets were identified using multi-locus microsatellite DNA analysis. Tree genotypes from mycorrhizas were matched to reference trees aboveground. Two trees were considered linked if they shared the same fungal genet(s). *The two Rhizopogon species each formed 13-14 genets, each colonizing up to 19 trees in the plot. Rhizopogon vesiculosus genets were larger, occurred at greater depths, and linked more trees than genets of R. vinicolor. Multiple tree cohorts were linked, with young saplings established within the mycorrhizal network of Douglas-fir veterans. A strong positive relationship was found between tree size and connectivity, resulting in a scale-free network architecture with small-world properties. *This mycorrhizal network architecture suggests an efficient and robust network, where large trees play a foundational role in facilitating conspecific regeneration and stabilizing the ecosystem.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".