Effects of ectomycorrhizal inoculants on survival and growth of interior Douglas-fir seedlings on reforestation sites and partially rehabilitated landings
Bibliographic record
Abstract
We studied the effects of commercially available (Laccaria laccata (Scop.:Fr.) Berk. & Br. and Rhizopogon parksii Smith (Oregon source)) and native (R. parksii (British Columbia source)) ectomycorrhizal (EM) inoculants on the survival and growth of commercially grown interior Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) seedlings outplanted on reforestation sites (burned piles and clearcuts) and partially rehabilitated (shallow- and deep-tilled to a depth of 15 and 50 cm, respectively) landings. We also examined the physical and chemical properties of the soil and the EM status and foliar element levels of noninoculated Douglas-fir seedlings to provide information on the growing conditions found on these types of sites. Inoculation treatments did not significantly increase survival and growth of Douglas-fir seedlings 2 years after outplanting. However, because the average percent EM colonization of inoculated seedlings at time of outplanting was low (36%), the beneficial effects of these inoculants may not have been attained. It is possible that nursery conditions partially account for the low EM colonization of inoculated seedlings. We therefore suggest that nurseries try to modify growing conditions to favor good EM formation before outplanting interior Douglas-fir. Benefits of inoculations on landings may have been restricted by the poor soil conditions, potentially toxic levels of Fe and Al, and competition from well-adapted native EM fungi.
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 | 0.000 | 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 | 0.001 |
| 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".