Lowbush blueberries in Northwestern Ontario as a commercial crop option in post-harvest peatlands
Bibliographic record
Abstract
Wild blueberries (Vaccinium angustifolium Ait.) were investigated as a possible reclamation tool for post harvest peatlands in Northwestern Ontario, by genetic fingerprinting using microsatellite markers, an examination of native blueberry soils in comparison with the GG3 peatland near Upsala, ON and identification of optimal soils in the GG3 peatland using GIS-based approach and multivariate statistics. The genetic diversity of natural populations of V. angustifolium Ait. was investigated using four microsatellite markers developed for Vaccinium corymbosum L. and compared to nineteen commercial wild blueberry plants. Fifty blueberry plants collected from across Northwestern Ontario were selected to obtain genetic fingerprints and to estimate genetic similarity by microsatellite analysis. The microsatellite data set was analyzed using POPGENE. Each of the microsatellite primers was polymorphic creating a total of 133 bands with the proportion of polymorphic loci at 97%, with none of them in all of the plant samples. The number of alleles per locus was 33.35 with a range of 13 to 51. The average genetic similarity was 97%. The corresponding nonmetric multidimensional scaling analysis supported the clusters and showed the separation of the commercial plants from the wild plants from Northwestern Ontario and distinct outliers, plants from Nipigon and Sioux Lookout. Low gene flow could be attributed to fragmented habitat due to their need for open sunlight and acid soils. Microsatellite analysis is a beneficial tool for genetic fingerprinting and identification, as well as useful for investigating genetic similarities and differences. \nWe studied the soil chemistry of eight different native blueberry stands across Northwestern Ontario and compared them to 210 peat soil samples from the GG3 \npeatland near Upsala, ON. Comparing these soils to the Canadian Soil Quality Guidelines revealed that arsenic levels for the site near Wawa could present phytotoxicity in blueberry plants. Nonmetric multidimensional scaling showed a distinct separation of blueberry soils from the peat soils along the second axis. Bulk density was one of the main differences and could be increased by tilling the mineral subsoil into the peat in the preparation for blueberry planting. Layers within the peat bog were difficult to separate in the statistical analysis. Leaf tissue analysis would help identify nutrient deficiencies better than soils analysis due to the perennial nature of the blueberry plants. Identification of optimal areas of soil within the GG3 peatland would ensure the success of using blueberries for reclamation. Using the GIS software, ArcGIS, nonmetric multidimensional scaling values for axis two were mapped for the seven individual layers of the peatland. A band, running from east to west in the middle of the peatland showed soil chemistry values closest to the native blueberry soils. An upper layer of the peat should be reserved for use in the blueberry reclamation as it would have the least issues with phytotoxicity due to high elemental content.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".