Media for Detecting Lactic Acid Spoilage Bacteria Isolated from Cured Cooked Meat Products
Bibliographic record
Abstract
食肉製品製造工場における汚染乳酸菌を対象に，簡便で高感度に検出する改良培地と培養条件を検討するとともに，RAPD-PCR 法などを利用した食肉製品製造工場における汚染源解析を試みた．（1） BCP 添加 APT broth からグルコース濃度，寒天濃度，緩衝能を調整した LA 培地は，既存の培地に比べ高感度に乳酸菌を検出できた．（2） LA 培地は，25°C で 48 時間培養することにより，食肉製品製造工場から分離した汚染乳酸菌を含む供試乳酸菌230株中の97%を検出し，さらに 24 時間培養することで全体の 99.6% を検出した．（3） LA 培地は試験管内の培地全体において指示薬の色の変化が鮮明になるため，LA 培地を用いることによって，効果的に汚染乳酸菌を検出できることが明らかとなった．（4） LA 培地を用いて食肉製品製造工場の汚染乳酸菌を検査，分離し，RAPD-PCR 法と 16SrDNA 解析を利用することにより，製品を汚染している乳酸菌の汚染源を特定できることが確認され，衛生改善に役立つことが実証された．
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".