Bibliographic record
Abstract
Too many kinds of tea are available in Senegal but our study will concern the two ones more used by Senega-lese people: the N.8147 and «the Special Gun Powder». We prepare tea according to the traditional method with w a t e r, sugar and mint. So for 4 persons, and for each kind of tea we prepare 3 tea pots: * The first one with 10,5 g of tea, 180 ml of water, the all boiled during 10 minutes, then add 25 g of sugar and finally serve according to taste, * The second one like the previous, but with 120 ml of water and the same content of tea (people can add some tea if we want), 10 minutes of boiling, 3 g of mint leaves (fresh or dry). The all boiled again during 5 minutes with 25 g of sugar before serving, * The third, which is the last one traditionally, is the continuation of the second teapot with only tea leaves in which we put 120 ml of water boiled during 10 minutes, 25 g of sugar added and serve. Sometimes people can add to the third teapot 5 g of dry tea and mint. The tools used to boil water are made of iron covered up by rust preventive. We can say that generally tea makers don’t measure exactly the weigh of dry tea, mint and sugar. It depends to the number of tea consumers, in this people can use the half, the quarter or the full drinking cup with dry tea. I t ’s the same for the mint, with 3 or 4 small boughs of fresh mint. As for the sugar they use 3 lumps of sugar for each full cup. A lump of sugar is about 4,17 g of weight. So the numbers we give here are averages. Traditionally in a drinking tea session, 3 teapots are prepared in succession as described above whatever the number of persons and to each one a cup containing about 30 ml of decocted tea is served. This also is an average, because we are estimating here the half of the drinking cup we use. So, we sent for analysis a cup of each teapot of each type of tea, that is to say 6 sample of teacup for 30 ml of water, sugar and 30 ml of decocted mint. The analysis was done at the fluoride Laboratory of the
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.237 | 0.139 |
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".