Extraire le jus de fruits de kiwi et de canneberges pour la détermination du Brix - Extracting fruit juice from kiwiberry and cranberry for Brix determination v1
Bibliographic record
Abstract
This protocol describes the simple yet robust preparation of fleshy fruits to enable the collection of sufficient volume of juice from fresh or frozen juice by sedimenting the fruit paste with centrifugation and collecting the the supernatant. The protocol was initially developed with kiwiberry and cranberries by CIEL https://www.ciel-cvp.ca/ in L'Assomption, Quebec. The protocol is run by batches (only limited by the number of wells in your rotor's centrifuge) and should yield between 200 ul and 1 ml of fruit juice in approximately 2 hours. Ce protocole décrit la préparation simple mais efficace de fruits charnus afin de permettre la collecte d'un volume suffisant de jus à partir de jus frais ou congelé, en sédimentant la pulpe de fruit par centrifugation et en recueillant le surnageant. Le protocole a été initialement développé avec des kiwis et des canneberges par le CIEL https://www.ciel-cvp.ca/ à L'Assomption, au Québec. Le protocole est exécuté par lots (limités uniquement par le nombre de puits du rotor de de votre centrifugeuse) et devrait permettre d'obtenir entre 200 ul et 1 ml de jus de fruit en environ 2 heures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.018 |
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".