The effect of <i>Frankia</i> and <i>Paxillus involutus</i> on the performance of <i>Alnus incana</i> subsp. <i>rugosa</i> in mine tailings
Bibliographic record
Abstract
The purpose of this study was to determine whether symbiotic nitrogen-fixing bacteria and mycorrhizal fungi act synergistically to improve plant performance when grown on heavy metal mine tailings. Seedlings were inoculated with Frankia, Paxillus involutus (Batsch) Fr., or a combination of both and grown in 100% peat, a 1:1 mix of peat and tailings, or 100% tailings for 20 weeks. Mortality of plants grown on pure tailings (15.0%) and peat–tailings (17.9%) was significantly greater than mortality of plants grown on peat (3.5%). The rate of nodulation and mycorrhizae formation decreased from 90.0% and 66.7%, respectively, on the peat, to 11.9% and 2.6% on the tailings. Frankia-inoculated plants grown on peat–tailings showed twice the mortality rate (38.5%) of any other inoculation treatment. Plants grown on media containing tailings had greater root/shoot ratios than plants grown on peat. Inoculating plants grown in the presence of tailings with either Frankia and (or) P. involutus increased root thickness. Inoculating plants with both symbionts increased colonization rates and shoot yield on the peat and peat–tailings media, suggesting that these symbionts act synergistically to improve plant performance. However, inoculating plants with Frankia decreased shoot relative growth rate in the early part of the experiment when plants were not fixing nitrogen, which, coupled with the higher mortality effect, suggests that nodule development is a stress for plants. It may be advisable that plants have fully functioning nodules before transplanting if they are to be used in revegetation programs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".