Spatial genetic structure of lowbush blueberry,<i>Vaccinium angustifolium</i>, in four fields in Maine
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Bibliographic record
Abstract
Expressed sequence tag – polymerase chain reaction (EST-PCR) molecular markers were used to infer spatial genetic structure of four lowbush blueberry ( Vaccinium angustifolium Ait.) fields in Maine. Genetic structure was quantified at three spatial scales: (1) within apparent clones (intrapatch), (2) among clones within a field, and (3) among fields separated by as much as 65 km. Of five “clones” or putative individuals examined in the intrapatch study, two showed complete genetic homogeneity within the patch, while three showed some band differences at their edges compared with their interiors. These differences at the edges, however, matched adjacent clones (so-called “intruders”), from which it was concluded that lowbush blueberry exhibits a fairly tight, phalanx clonal architecture with no evidence of invasive seedling establishment within clones. No significant correlation between genetic and physical distance was found among clones within fields via several statistical approaches. Significant among-field genetic differentiation was found via AMOVA (Φ PT = 8.4%; p ≤ 0.01) based upon transect samples across four fields ranging from 12.5 to 65 km apart. Principal component analysis and spatial autocorrelation (SA) corroborated these findings. Significant positive SA was found at the within-field distance class of <350 m, but SA decreased to an insignificant value by the first interfield distance of 12.5 km. A special form of SA analysis was employed to detect “hotspots” of genetic similarity between pairs of adjacent clones in two fields. Results indicated that 5 of 23 pairs of clones (21.7%) were genetically similar to each other, while the majority of pairs (18 of 23; 78.3%) showed random, decreasing patterns of genetic similarity. Results are discussed in terms of clonal dynamics including architecture, seedling recruitment, and inferred pollen or seed dispersal distances.
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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 | 0.000 |
| 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 it