Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential
Bibliographic record
Abstract
The availability of Scots pine seeds (Pinus sylvestris L.) with high germinability is necessary for artificial forest regeneration. In this work, Scots pine seed orchard seeds were magnetic resonance (MR) imaged to noninvasively investigate the association of the anatomical images and quantitative relaxation times with the structure and germinability of the seeds. Relaxation time differences compared to the germination day were also investigated. The average whole seed relaxation times T1 (two methods), T2, and [Formula: see text] were 430 ± 59, 660 ± 20, 14 ± 1.7, and 0.83 ± 0.33 ms, respectively. It was observed that the seed structures had statistically significant (p < 0.05) differences in relaxation times, while no differences could be observed in relation to the rate of seed germination. Furthermore, the obtained data were compared to radiographs. Empty seeds were observed to provide a minimal MRI signal, whereas intact and mechanically damaged seeds provided a profound signal with distinguishable structures. The mechanically hardest region, i.e., the seed coat, was not visible in MRI as opposed to radiographs. Some seeds determined to be mechanically damaged by radiography were able to germinate, and mechanical faults could be distinguished in MRI. As such, MRI can be seen complementary to the currently used methods to optimize seed sorting and to interpret germination potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".