Peer Review #2 of "Commercial arbuscular mycorrhizal fungal inoculant failed to establish in a vineyard despite priority advantage (v0.1)"
Bibliographic record
Abstract
Background.Arbuscular mycorrhizal (AM) fungi associate with most plants and can increase nutrient uptake.As a result, commercial inoculants called "biofertilizers" containing AM fungi have been developed and marketed to increase plant performance.However, successful establishment of these inoculants remains a challenge, and may be negatively impacted by competition with fungi already present (priority effects).Perennial agriculture may be more amenable if inoculants can be successfully established on crops prior to field planting. Methods.Here, we inoculate grapevine (Vitis vinifera) with a commercial inoculant in three treatments designed to manipulate the strength and direction of priority effects and quantified the abundance of the fungal strain before and after introduction using digital droplet PCR (ddPCR).Results.We found that the introduced strain did not establish in any treatment, even with priority advantage, and inoculated vines did not differ in performance from non-inoculated vines.Fungal abundance was not greater than in pre-inoculation soil samples during any of the five years sampled and may have been impaired by high available phosphorus levels in the soil.This study highlights the need to understand and evaluate how the management of the agricultural system will affect establishment before introduction of "biofertilizers", which is often unpredictable.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.032 |
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".