Development and Standardization of Rapid and Efficient Seed Germination Protocol for Cannabis sativa
Bibliographic record
Abstract
Cannabis seed germination is an important process for growers and researchers alike. Many biotechnological applications require a reliable sterile method for seed germination. This protocol outlines a seed germination procedure for Cannabis sativa using a hydrogen peroxide (H2O2) solution as liquid germination media. In this protocol, all three steps including seed sterilization, germination, and seedlings development were carried out in an H2O2 solution of different concentrations; 1% H2O2 solution showed the fastest and the most efficient germination. This protocol also exhibited high germination efficiency for very old cannabis seeds with lower viability. Overall, this protocol demonstrates superior germination compared to water control and reduces the risk of contamination, making it suitable for tissue culture and other sensitive applications., [摘要]大麻种子的发芽对种植者和研究者都是重要的过程。许多生物技术应用需要可靠的无菌方法来发芽种子。该协议概述了使用过氧化氢（H 2 O 2 ）溶液作为液体发芽介质的苜蓿种子的发芽过程。在这种PROT ocol，所有三个步骤，包括种子消毒，发芽，和幼苗发育中进行了一个ħ 2 ö 2种不同浓度的溶液; 1％H 2 O 2 解决方案显示出最快，最有效的发芽。该协议还显示了对于具有较低生存能力的非常老的大麻种子的高发芽效率。总的来说，与水控制相比，该协议证明了更好的发芽能力，并降低了污染的风险，使其适合组织培养和其他敏感应用。[背景技术]大麻，否则称为大麻或大麻，是其中男性/女性性别由异型染色体（X和Y）确定的年主要雌雄异株的开花植物（德特等人，2020）。大麻种植有多种农业用途。几乎所有大麻植物的部分都被使用，种子用作食物，茎用作纤维，花/叶用作药物。花会产生大麻素和芳香化合物的混合物，这些化合物因其治疗和娱乐作用而受到重视（Chandra等人，2017）。大麻植物通过插条或通过种子发芽而无性繁殖。种子萌发是研究人员，育种非常重要，而且种植者的喜爱，尤其是从优良品种的种子可能是非常昂贵的和有价值的。此外，较老的种子发芽率可能降低，而细菌和真菌污染会危害发芽，特别是在种子发芽进行组织培养繁殖时。为了解决这些问题，我们已经开发了一种快速，无菌，使用1％的过氧化氢（H，高效的种子萌发协议2 ö 2 ）的解决方案。在这个协议中，所有的三个步骤，包括种子消毒，发芽，和幼苗发育，在1％H进行了2 ö 2溶液。这提出一个比其他显著优点消毒剂，如氯化汞或漂白剂，这需要种子的另外的洗涤和MS固体培养基上一个单独的步骤萌发。与水控制相比，我们的实验方案产生了更快的发芽和更高的种子发芽率，没有细菌或真菌污染，使其适合组织培养和其他敏感应用。与以前的发芽方法相比，发芽的外观需要4-7天，幼苗发育需要5-15天（Wielgus等，2008和其中的参考文献），我们的发芽方法导致发芽的外观在1天之内，使我们能够获得大麻苗一个很短的时间（3-7天），以最小的努力。该协议对于非常低活力的非常古老的大麻种子的发芽也非常有效。
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".