Apatite Stimulates the Deposition of Glomalin-Related Soil Protein in a Lowbush Blueberry Commercial Field
Bibliographic record
Abstract
Many wind-sensitive and unproductive soils could benefit from increased glomalin-related soil protein (GRSP), an operationally defined soil protein pool known to improve soil quality and nutrient storage. We expect at least part of this GRSP fraction to originate from fungal biomass. Although P-rich minerals such as apatite are known to increase C allocation from plants to mycorrhizal fungi, there are no studies directly linking apatite with GRSP. We investigated the effect of apatite on GRSP deposition rates in a cultivated field of wild lowbush blueberry (Vaccinium angustifolium Aiton; Vaccinium myrtilloides Michx.) in the Saguenay‒Lac-Saint-Jean region of Quebec (Canada). A field incubation technique (145 days) using sterilized porous sand bags (50 µm pores) was used to measure in situ easily extractable GRSP (EE-GRSP) deposition rates from bags with (n = 10) and without (n = 10) apatite. Half of the bags (n = 10) were also soaked in Proline® 480 SC (Bayer CropScience, Calgary, Alberta, Canada) (Prothioconazole) to determine if EE-GRSP deposition rates were affected by this commonly applied fungicide. Our results indicated that adding apatite into sand bags significantly increased (+70%) EE-GRSP deposition rates, whereas soaking the bags in fungicide had no significant effect. Although the direct linkage between GRSP and lowbush blueberry plants remains to be detailed, our study reports for the first time GRSP concentrations from lowbush blueberry soils. Implications of these findings are discussed.
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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) | 0.000 | 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) | 0.001 | 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".