Application of proteomics to the study of pollination drops
Bibliographic record
Abstract
PREMISE OF THE STUDY: Pollination drops are a formative component in gymnosperm pollen-ovule interactions. Proteomics offers a direct method for the discovery of proteins associated with this early stage of sexual reproduction. • METHODS: Pollination drops were sampled from eight gymnosperm species: Chamaecyparis lawsoniana (Port Orford cedar), Ephedra monosperma, Ginkgo biloba, Juniperus oxycedrus (prickly juniper), Larix ×marschlinsii, Pseudotsuga menziesii (Douglas-fir), Taxus ×media, and Welwitschia mirabilis. Drops were collected by micropipette using techniques focused on preventing sample contamination. Drop proteins were separated using both gel and gel-free methods. Tandem mass spectrometric methods were used including a triple quadrupole and an Orbitrap. • RESULTS: Proteins are present in all pollination drops. Consistency in the protein complement over time was shown in L. ×marschlinsii. Representative mass spectra from W. mirabilis chitinase peptide and E. monosperma serine carboxypeptidase peptide demonstrated high quality results. We provide a summary of gymnosperm pollination drop proteins that have been discovered to date via proteomics. • DISCUSSION: Using proteomic methods, a dozen classes of proteins have been identified to date. Proteomics presents a way forward in deepening our understanding of the biological function of pollination drops.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".