Alginate gel entrapped ectomycorrhizal inoculum promoted growth of cuttings of <i>Eucalyptus</i> clones under nursery conditions
Bibliographic record
Abstract
Plant inoculation with ectomycorrhizal fungi (EMF) maximizes the productive potential of forest stands. Thus, the inoculation efficiency of calcium alginate gel entrapped EMF vegetative mycelium was evaluated in a commercial nursery using cuttings of Eucalyptus clones GG100 and GG680. The cuttings were inoculated with Pisolithus microcarpus G. Cunn. (Cooke & Massee), Hysterangium gardneri E. Fisch., and Scleroderma areolatum Ehrenb. The cuttings were cultivated under low phosphate fertilization and compared with uninoculated control treatments with reduced phosphate (low P control) and full phosphate (high P control) fertilization. Pisolithus microcarpus inoculation increased shoot height, root collar diameter, shoot dry mass, total dry mass, and frequency of maximum score for root ball formation of the two clones compared with the low P control treatment. Also, in relation to the low P control treatment, H. gardneri inoculation increased shoot dry mass in GG100 rooted cuttings. Scleroderma areolatum inoculation did not enhance any characteristic of Eucalyptus rooted cuttings. Inoculation of vegetative mycelium with EMF impregnated in calcium alginate gel intensified rooted cutting growth in a commercial Eucalyptus nursery and decreased the phosphate dose required. Based on the comparison of two Eucalyptus clones, efficiency of the inoculants in promoting benefits depends on the fungus and the Eucalyptus clone. Pisolithus microcarpus is most promising for inoculation in Eucalyptus cuttings.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".