Dissection fonctionnelle et spatiale de la biosynthèse du galanthamine chez les Amaryllidacées : une approche combinant N-méthyltransférase et imagerie par spectrométrie de masse = Functional and spatial dissection of galanthamine biosynthesis in Amaryllidaceae : a N-methyltransferase and mass spectrometry imaging approach
Bibliographic record
Abstract
chapter, I'd like to take a moment to thank the many wonderful beingsboth human and otherwise-who've helped me along the way.Whether through their wisdom, patience, caffeine offerings, emotional support, or just by silently existing and making the world a little less chaotic, your contributions-direct or delightfully indirect-have meant more than words can express.I owe immense gratitude to my supervisor, Prof. Isabel Desgagné-Penix.Thank you for your constant guidance, for always being available with a listening ear and thoughtful advice, and above all, for your empathy and unwavering support through every twist and turn of this long academic road.Your mentorship, patience, and kindness have been a lighthouse during stormy times.To my Amma and Appachchi-thank you for everything.For the life you gave me, the values you taught, the kindness and sacrifices, and for tolerating the endless stubbornness of your curious, occasionally chaotic son.Your love and strength are the foundation on which all of this stands.Also to my sister, for your support and constant encouragement through all the ups and downs.Dr. Natacha Mérindol, from the moment I arrived, your warmth and good energy made me feel right at home.Thank you for taking me under your wing, teaching me so much-from techniques to scientific thinkinghelping shape my project and manuscripts, and being a steady presence through all my highs and lows.Thank you also for tolerating my occasional bouts of strong personality and wild ideas-and, of course, v for all your hearty homemade food and garden vegetables that brought extra joy (and nutrition!) to my life.To Prof. Christian Janfelt, thank you for welcoming me into your lab in Copenhagen, for your mentorship, your calm and kind demeanor, and for teaching me from scratch with such generosity and patience.You made my internship a truly enriching and memorable experience.Manoj Koirala, I don't even know where to start.From day one in the lab, you've been like a brother-teaching me everything from A to Z, constantly challenging me with questions I should probably know, and feeding me delicious Nepali food.I still remember: "Sameera, have you tried momo before?Then let's go to my place tonight."Unforgettable.Sarah-Eve Gélinas, thank you for always being there with technical support, kind words, and the legendary phrase: "You'll be fine, Sameera."It always helped more than you know.To the funding agencies-Canada Research Chair, NSERC, MITACS, UQTR, and all the organizations that supported me financially-thank you!Without your help
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".