ᱵᱷᱨᱚᱢ ᱠᱚ · ᱵᱷᱨᱚᱢ ᱠᱚ · 6 ᱢᱤᱱᱤᱴ

ᱨᱩᱞᱮᱴ ᱰᱤᱞᱟᱨ ᱠᱚ ᱮᱞ ᱱᱤᱥᱟᱹᱱᱟ ᱫᱟᱲᱮᱭᱟᱜ ᱠᱚᱣᱟ ᱥᱮ? ᱵᱷᱨᱚᱢ

“ᱰᱤᱞᱟᱨ ᱫᱚ ᱵᱟᱰᱟᱭ ᱠᱟᱛᱮ ᱡᱤᱨᱚ ᱨᱮ ᱮ ᱫᱚᱦᱚ ᱞᱮᱫᱟ᱾” ᱰᱤᱞᱟᱨ ᱫᱚ ᱚᱠᱟ ᱨᱮ ᱦᱚᱸ ᱵᱟᱝ ᱮ ᱫᱚᱦᱚ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ᱾

ᱠᱷᱟᱴᱚ ᱛᱮᱞᱟ

ᱨᱩᱞᱮᱴ ᱰᱤᱞᱟᱨ ᱠᱚ ᱮᱞ ᱱᱤᱥᱟᱹᱱᱟ ᱫᱟᱲᱮᱭᱟᱜ ᱠᱚᱣᱟ ᱥᱮ? ᱵᱟᱝ᱾ ᱰᱤᱞᱟᱨ ᱫᱚ ᱵᱚᱞ ᱮᱰᱮ ᱨᱮᱭᱟᱜ ᱞᱚᱜᱚᱱ ᱟᱨ ᱨᱚᱴᱚᱨ ᱨᱮᱭᱟᱜ ᱞᱚᱜᱚᱱ ᱮ ᱥᱟᱢᱵᱷᱟᱞᱟ, ᱵᱚᱞ ᱡᱟᱦᱟᱸ ᱰᱤᱯᱷᱞᱮᱠᱴᱚᱨ ᱨᱮ ᱫᱷᱟᱠᱟ ᱟᱭ ᱥᱮ ᱪᱮᱫ ᱞᱮᱠᱟ ᱫᱩᱯᱩᱡᱚᱜ-ᱟ ᱚᱱᱟ ᱫᱚ ᱵᱟᱝ᱾ ᱜᱤᱨᱤᱡ ᱛᱟᱭᱚᱢ ᱪᱟᱹᱛᱟᱹᱨ ᱫᱚ ᱵᱟᱨᱥᱮ ᱥᱮᱫ 10 ᱠᱷᱚᱱ 15 ᱠᱷᱟᱱᱟ ᱦᱟᱹᱵᱤᱡ ᱯᱟᱥᱱᱟᱣᱚᱜ-ᱟ᱾ ᱡᱟᱦᱟᱸ ᱰᱤᱞᱟᱨ ᱦᱚᱸ ᱢᱤᱫ ᱠᱷᱟᱱᱟ ᱵᱟᱝ ᱮ ᱱᱤᱥᱟᱹᱱᱟ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ, ᱟᱨ ᱥᱴᱩᱰᱤᱭᱳ ᱫᱚ ᱥᱮᱠᱴᱚᱨ ᱨᱮᱭᱟᱜ ᱢᱤᱫ ᱞᱮᱠᱟᱱ ᱦᱚᱸ ᱵᱟᱝ ᱦᱩᱭ ᱫᱟᱲᱮᱭᱟᱜ ᱞᱮᱠᱟ ᱠᱚ ᱵᱮᱱᱟᱣᱟ᱾

ᱰᱤᱞᱟᱨ ᱥᱟᱢᱵᱷᱟᱞᱟ
ᱵᱚᱞ ᱮᱰᱮ, ᱨᱚᱴᱚᱨ ᱮᱞᱠᱟ
ᱰᱤᱞᱟᱨ ᱵᱟᱝ ᱥᱟᱢᱵᱷᱟᱞᱟ
ᱰᱤᱯᱷᱞᱮᱠᱴᱚᱨ ᱫᱷᱟᱠᱟ, ᱯᱷᱨᱮᱴ ᱫᱩᱯᱩᱡ
ᱪᱟᱹᱛᱟᱹᱨ
ᱵᱟᱨᱥᱮ ᱥᱮᱫ 10–15 ᱠᱷᱟᱱᱟ
ᱯᱷᱚᱭᱥᱚᱞᱟ
ᱵᱷᱨᱚᱢ

ᱫᱟᱵᱤ

ᱢᱤᱫ ᱦᱟᱹᱱᱤ ᱜᱷᱩᱨᱟᱹᱣ ᱛᱟᱭᱚᱢ, ᱢᱟᱨᱟᱝ ᱠᱟᱛᱮ ᱡᱤᱨᱚᱪᱟᱠᱟᱡᱤᱨᱚᱢᱤᱫᱜᱮ ᱦᱟᱹᱨᱭᱟᱹᱲ ᱠᱷᱟᱱᱟ ᱡᱟᱦᱟᱸ ᱥᱚᱢᱟᱱ ᱴᱟᱠᱟ, ᱰᱚᱡᱮᱱ ᱟᱨᱵᱟᱝ ᱠᱚᱞᱚᱢ ᱵᱟᱡᱤ ᱵᱟᱭ ᱠᱚᱵᱷᱟᱨᱟ; ᱠᱮᱥᱤᱱᱳ ᱵᱟᱲᱛᱤ ᱨᱮᱭᱟᱜ ᱢᱩᱞ᱾ ᱮᱴᱟᱜ ᱧᱩᱛᱩᱢ: ᱥᱤᱝᱜᱚᱞ ᱡᱤᱨᱚ, 0᱾ᱯᱩᱨᱟᱹ ᱯᱟᱲᱦᱟᱣ → ᱨᱮ, ᱠᱷᱮᱞᱚᱸᱰ ᱠᱚ ᱫᱚᱨᱚᱢ ᱫᱚᱨᱚᱢ ᱵᱤᱥᱣᱟᱥ ᱠᱚ ᱮᱫᱟ ᱡᱮ ᱰᱤᱞᱟᱨ ᱫᱚ ᱵᱚᱞᱥᱟᱡ ᱥᱟᱢᱟᱱᱵᱚᱞᱨᱚᱴᱚᱨ ᱨᱮᱭᱟᱜ ᱩᱞᱴᱟᱹ ᱥᱮᱫ ᱴᱨᱮᱠ ᱨᱮ ᱜᱤᱰᱤ ᱦᱩᱰᱤᱧ ᱵᱚᱞ᱾ ᱱᱤᱛᱚᱜᱟᱜ ᱵᱚᱞ ᱫᱚ ᱟᱭᱣᱚᱨᱤᱱ ᱟᱨᱵᱟᱝ ᱴᱮᱯᱷᱞᱚᱱ ᱨᱮᱭᱟᱜ, 18–21 mm᱾ᱯᱩᱨᱟᱹ ᱯᱟᱲᱦᱟᱣ → ᱮ ᱱᱤᱥᱟᱹᱱᱟ ᱞᱮᱫᱟ᱾ ᱱᱚᱶᱟ ᱵᱤᱥᱣᱟᱥ ᱫᱚ ᱞᱮᱵ ᱨᱮᱭᱟᱜ ᱰᱤᱞᱟᱨ-ᱪᱤᱱᱦᱟᱹ ᱚᱱᱩᱢᱟᱱ ᱠᱷᱚᱱ ᱜᱚᱲᱚ ᱧᱟᱢᱟ, ᱡᱟᱦᱟᱸ ᱫᱚ ᱠᱩᱞᱤ ᱮᱫᱟ ᱡᱮ ᱰᱤᱞᱟᱨ ᱨᱮᱭᱟᱜ ᱦᱟᱵᱤᱥ ᱫᱚ ᱜᱤᱨᱤᱡ ᱨᱮᱭᱟᱜ ᱥᱮᱠᱴᱚᱨᱪᱟᱠᱟᱥᱮᱠᱴᱚᱨᱪᱟᱠᱟ ᱨᱮ ᱛᱟᱭᱚᱢ ᱛᱟᱭᱚᱢ ᱠᱷᱟᱱᱟ ᱨᱮᱭᱟᱜ ᱢᱤᱫ ᱜᱩᱞᱟᱹᱭ ᱦᱟᱹᱴᱤᱧ, ᱞᱮᱭᱟᱣᱴ ᱨᱮᱭᱟᱜ ᱜᱩᱴᱤ ᱠᱷᱚᱱ ᱟᱞᱟᱜ᱾ᱯᱩᱨᱟᱹ ᱯᱟᱲᱦᱟᱣ → ᱤᱴᱤᱡ ᱞᱟᱦᱟ ᱠᱟᱛᱷᱟ ᱞᱟᱹᱭ ᱫᱟᱲᱮᱭᱟᱜ ᱞᱮᱠᱟ ᱵᱮᱱᱟᱣᱟ ᱥᱮ ᱵᱟᱝ᱾ ᱚᱱᱟ ᱚᱱᱩᱢᱟᱱ ᱫᱚ ᱥᱮᱠᱴᱚᱨ ᱟᱨ ᱥᱴᱮᱴᱤᱥᱴᱤᱠᱥ ᱵᱟᱵᱚᱫ ᱠᱟᱱᱟ; ᱵᱷᱨᱚᱢ ᱫᱚ ᱠᱷᱟᱱᱟᱥᱟᱡ ᱥᱟᱢᱟᱱᱠᱷᱟᱱᱟᱨᱚᱴᱚᱨ ᱨᱮᱭᱟᱜ 37 ᱮᱞ ᱚᱞ ᱠᱷᱟᱱᱟ ᱠᱷᱚᱱ ᱢᱤᱫ᱾ ᱮᱴᱟᱜ ᱧᱩᱛᱩᱢ: ᱥᱞᱚᱴ, ᱠᱟᱯ᱾ᱯᱩᱨᱟᱹ ᱯᱟᱲᱦᱟᱣ → ᱟᱨ ᱩᱫᱮᱥ ᱵᱟᱵᱚᱫ᱾

ᱰᱤᱞᱟᱨ ᱪᱮᱫ ᱮ ᱠᱟᱹᱢᱤ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ

ᱢᱤᱫ ᱮᱞᱠᱟ ᱛᱮ ᱨᱚᱴᱚᱨᱥᱟᱡ ᱥᱟᱢᱟᱱᱨᱚᱴᱚᱨᱪᱟᱠᱟ ᱨᱮᱭᱟᱜ ᱜᱷᱩᱨᱟᱹᱣᱚᱜ ᱦᱟᱹᱴᱤᱧ ᱡᱟᱦᱟᱸ ᱨᱮ ᱠᱷᱟᱱᱟ ᱠᱚ ᱢᱮᱱᱟᱜ-ᱟ᱾ᱯᱩᱨᱟᱹ ᱯᱟᱲᱦᱟᱣ → ᱨᱮᱭᱟᱜ ᱞᱚᱜᱚᱱ ᱴᱷᱤᱠ, ᱟᱨ ᱵᱟᱪᱷᱟᱣ ᱟᱠᱟᱱ ᱡᱚᱨ ᱛᱮ ᱵᱚᱞ ᱮᱰᱮ᱾ ᱵᱟᱨᱭᱟ ᱜᱮ ᱡᱷᱚᱛᱚ ᱞᱮᱠᱟᱛᱮ: ᱛᱤ ᱫᱚ ᱢᱮᱥᱤᱱ ᱫᱚ ᱵᱟᱝ ᱠᱟᱱᱟ᱾ ᱡᱩᱫᱤ ᱢᱤᱫ ᱰᱤᱞᱟᱨ ᱯᱩᱨᱟᱹ ᱢᱤᱫ ᱞᱮᱠᱟᱱ ᱛᱟᱦᱮᱸᱱ ᱠᱷᱟᱱ, ᱵᱚᱞ ᱫᱚ ᱮᱰᱮ ᱛᱩᱞᱟᱹ ᱢᱤᱫ ᱞᱮᱠᱟᱱ ᱴᱷᱟᱶ ᱨᱮ ᱴᱨᱮᱠ ᱮ ᱵᱟᱹᱜᱤᱭᱟ, ᱟᱨ ᱨᱚᱴᱚᱨ ᱫᱚ ᱢᱤᱫ ᱞᱮᱠᱟᱱ ᱫᱚᱥᱟ ᱨᱮ ᱛᱟᱦᱮᱸᱱᱟ᱾ ᱱᱚᱶᱟ ᱜᱮ ᱜᱷᱩᱨᱟᱹᱣ ᱨᱮᱭᱟᱜ ᱴᱷᱤᱠ ᱦᱟᱹᱴᱤᱧ ᱠᱟᱱᱟ, ᱟᱨ ᱱᱚᱶᱟ ᱦᱟᱹᱴᱤᱧ ᱨᱮ ᱜᱮ ᱰᱤᱞᱟᱨ ᱟᱥᱚᱨ ᱮ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ᱾

ᱨᱩᱞᱮᱴ ᱨᱚᱴᱚᱨ ᱨᱮᱭᱟᱜ ᱞᱚᱜᱚᱱ

ᱰᱤᱞᱟᱨ ᱪᱮᱫ ᱵᱟᱝ ᱮ ᱠᱟᱹᱢᱤ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ

ᱤᱨᱟᱹᱞ ᱰᱤᱯᱷᱞᱮᱠᱴᱚᱨᱥᱟᱡ ᱥᱟᱢᱟᱱᱰᱤᱯᱷᱞᱮᱠᱴᱚᱨᱮᱯᱨᱚᱱ ᱨᱮᱭᱟᱜ ᱞᱚᱦᱟ ᱟᱴᱠᱟᱣ ᱡᱤᱱᱤᱥ ᱠᱚ ᱠᱷᱚᱱ ᱢᱤᱫ, ᱡᱟᱦᱟᱸ ᱧᱩᱨᱚᱜ ᱵᱚᱞ ᱮᱴᱟᱜ ᱥᱮᱫ ᱥᱮᱱ ᱵᱚᱫᱚᱞᱟ᱾ ᱮᱴᱟᱜ ᱧᱩᱛᱩᱢ: ᱞᱟᱣᱠᱟ, ᱦᱤᱨᱟ, ᱯᱤᱱ᱾ᱯᱩᱨᱟᱹ ᱯᱟᱲᱦᱟᱣ → ᱠᱷᱚᱱ ᱚᱠᱟ ᱨᱮ ᱵᱚᱞ ᱫᱷᱟᱠᱟ ᱟᱭ, ᱪᱮᱫ ᱠᱚᱬ ᱨᱮ, ᱥᱮ ᱯᱷᱨᱮᱴᱥᱟᱡ ᱥᱟᱢᱟᱱᱯᱷᱨᱮᱴᱪᱟᱠᱟ ᱨᱮ ᱵᱟᱨ ᱠᱷᱟᱱᱟ ᱛᱟᱞᱟ ᱨᱮᱭᱟᱜ ᱞᱚᱦᱟ ᱟᱲ᱾ᱯᱩᱨᱟᱹ ᱯᱟᱲᱦᱟᱣ → ᱠᱚ ᱪᱮᱛᱟᱱ ᱛᱮ ᱪᱮᱫ ᱞᱮᱠᱟ ᱫᱩᱯᱩᱡᱚᱜ-ᱟ, ᱱᱚᱶᱟ ᱥᱟᱢᱵᱷᱟᱞ᱾ ᱱᱚᱶᱟ ᱫᱚ ᱜᱤᱨᱤᱡ ᱚᱠᱛᱚ ᱨᱮᱭᱟᱜ ᱢᱤᱞᱤᱢᱤᱴᱚᱨ ᱴᱷᱟᱶ ᱨᱮ ᱱᱤᱨᱵᱷᱚᱨ ᱟᱨ ᱵᱮᱱᱟᱣ ᱞᱮᱠᱟᱛᱮ ᱜᱷᱩᱨᱟᱹᱣ ᱨᱮᱭᱟᱜ ᱦᱩᱞᱟᱹᱢ ᱦᱟᱹᱴᱤᱧ ᱠᱟᱱᱟ᱾ ᱱᱟᱣᱟ ᱪᱟᱠᱟ ᱨᱮ ᱢᱟᱯ ᱟᱠᱟᱱ ᱪᱟᱹᱛᱟᱹᱨ ᱵᱟᱸᱴᱟᱣ ᱫᱚ ᱢᱩᱪᱟᱹᱫ ᱠᱷᱟᱱᱟ ᱜᱤᱨᱤᱡ ᱴᱷᱟᱶ ᱜᱷᱮᱨᱟ 20 ᱠᱷᱚᱱ 30 ᱠᱷᱟᱱᱟ ᱨᱮ ᱯᱟᱥᱱᱟᱣᱟ᱾ ᱡᱟᱦᱟᱸ ᱰᱤᱞᱟᱨ ᱠᱷᱩᱥᱤ ᱞᱮᱠᱟᱛᱮ ᱢᱤᱫ ᱠᱷᱟᱱᱟ ᱮ ᱱᱤᱥᱟᱹᱱᱟ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ, ᱩᱱᱤ ᱫᱚ ᱢᱤᱫ ᱵᱷᱟᱣᱛᱤᱠ ᱵᱟᱝ ᱦᱩᱭ ᱫᱟᱲᱮᱭᱟᱜ ᱠᱟᱛᱷᱟ ᱠᱟᱱᱟ, ᱵᱮᱭᱟᱹᱢᱟᱱ ᱫᱚ ᱵᱟᱝ᱾

ᱫᱷᱟᱯᱰᱤᱞᱟᱨ ᱥᱟᱢᱵᱷᱟᱞᱨᱮᱡᱟᱞᱴ ᱨᱮ ᱟᱥᱚᱨ
ᱨᱚᱴᱚᱨ ᱮᱞᱠᱟᱡᱷᱚᱛᱚ ᱞᱮᱠᱟᱛᱮᱠᱷᱟᱱᱟ ᱠᱚ ᱪᱮᱫ ᱞᱮᱠᱟ ᱞᱚᱜᱚᱱ ᱯᱟᱨᱚᱢᱚᱜ-ᱟ ᱚᱱᱟ ᱴᱷᱤᱠ ᱟᱭ
ᱮᱰᱮ ᱡᱚᱨᱡᱷᱚᱛᱚ ᱞᱮᱠᱟᱛᱮᱛᱤᱱᱟᱹᱜ ᱜᱷᱮᱨᱟ, ᱜᱤᱨᱤᱡ ᱡᱷᱚᱛᱚ ᱛᱤᱥ ᱦᱩᱭᱩᱜ-ᱟ ᱚᱱᱟ ᱴᱷᱤᱠ ᱟᱭ
ᱮᱰᱮ ᱴᱷᱟᱶᱱᱤᱭᱚᱢ ᱛᱮ ᱴᱷᱤᱠ (ᱢᱩᱪᱟᱹᱫ ᱡᱤᱛ ᱮᱞ)ᱡᱚᱛᱚ ᱠᱚ ᱵᱟᱰᱟᱭ ᱠᱟᱫᱟ
ᱜᱤᱨᱤᱡ ᱴᱷᱟᱶᱥᱩᱡᱷᱤ ᱵᱟᱝ, ᱪᱮᱛᱟᱱ ᱨᱮᱭᱟᱜ ᱛᱮᱵᱮᱥ ᱨᱮ ᱢᱤᱫ ᱥᱮᱠᱴᱚᱨ, ᱟᱹᱰᱤ ᱢᱤᱫ ᱞᱮᱠᱟᱱ ᱥᱟᱶ
ᱰᱤᱯᱷᱞᱮᱠᱴᱚᱨ ᱫᱷᱟᱠᱟᱵᱟᱹᱱᱩᱜ-ᱟᱪᱟᱠᱟ ᱨᱮᱭᱟᱜ ᱯᱮ ᱦᱟᱹᱴᱤᱧ ᱨᱮ ᱢᱤᱫ ᱦᱟᱹᱴᱤᱧ ᱦᱟᱹᱵᱤᱡ ᱪᱟᱹᱛᱟᱹᱨ
ᱯᱷᱨᱮᱴ ᱫᱩᱯᱩᱡᱵᱟᱹᱱᱩᱜ-ᱟᱟᱨᱦᱚᱸ ᱪᱟᱹᱛᱟᱹᱨ
ᱢᱩᱪᱟᱹᱫ ᱠᱷᱟᱱᱟᱵᱟᱹᱱᱩᱜ-ᱟᱡᱚᱛᱚ ᱠᱟᱹᱢᱤ ᱞᱟᱹᱜᱤᱫ 1/37

ᱨᱩᱞᱮᱴ ᱨᱮ ᱠᱷᱚᱞᱨᱩᱞᱮᱴ ᱵᱚᱞ ᱨᱮᱭᱟᱜ ᱯᱷᱤᱡᱤᱠᱥ

ᱠᱮᱥᱤᱱᱳ ᱦᱚᱸ ᱱᱚᱶᱟ ᱪᱮᱫᱟᱜ ᱵᱟᱝ ᱥᱟᱱᱟᱭᱮᱫᱟ

ᱵᱚᱞ ᱚᱠᱟ ᱨᱮ ᱥᱮᱴᱮᱨᱚᱜ-ᱟ ᱵᱟᱝ ᱧᱮᱞ ᱠᱟᱛᱮ, ᱠᱮᱥᱤᱱᱳ ᱫᱚ ᱡᱚᱛᱚ ᱜᱷᱩᱨᱟᱹᱣ ᱨᱮ ᱡᱚᱛᱚ ᱵᱟᱡᱤ ᱦᱤᱥᱟᱹᱵᱴᱟᱠᱟᱡᱚᱛᱚ ᱵᱟᱡᱤ ᱦᱤᱥᱟᱹᱵᱢᱤᱫ ᱥᱮᱥᱚᱱ ᱟᱨᱵᱟᱝ ᱚᱠᱛᱚ ᱨᱮ ᱡᱚᱛᱚ ᱵᱟᱡᱤ ᱴᱟᱠᱟ, ᱫᱚᱦᱲᱟ ᱞᱟᱜᱟᱣ ᱡᱤᱛ ᱴᱟᱠᱟ ᱥᱮᱞᱮᱫ᱾ ᱮᱴᱟᱜ ᱧᱩᱛᱩᱢ: ᱦᱮᱱᱰᱮᱞ, ᱮᱠᱥᱚᱱ᱾ᱯᱩᱨᱟᱹ ᱯᱟᱲᱦᱟᱣ → ᱨᱮᱭᱟᱜ 2.70% ᱮ ᱧᱟᱢᱟ᱾ ᱨᱮᱡᱟᱞᱴ ᱪᱟᱞᱟᱣᱤᱭᱟᱹ ᱰᱤᱞᱟᱨ ᱫᱚ ᱠᱮᱥᱤᱱᱳ ᱨᱮᱭᱟᱜ ᱜᱟᱦᱠᱤ ᱠᱚ ᱠᱷᱚᱱ ᱮ ᱪᱩᱨᱤ ᱛᱟᱦᱮᱸᱱᱟ, ᱠᱮᱥᱤᱱᱳ ᱞᱟᱹᱜᱤᱫ ᱡᱟᱦᱟᱸ ᱞᱟᱵᱷ ᱵᱟᱹᱱᱩᱜ ᱠᱟᱛᱮ ᱟᱨ ᱟᱹᱰᱤ ᱢᱟᱨᱟᱝ ᱟᱹᱭᱤᱱ ᱡᱚᱠᱷᱤᱢ ᱥᱟᱶ᱾ ᱥᱴᱩᱰᱤᱭᱳ ᱠᱚ ᱡᱚᱛᱚ ᱜᱷᱩᱨᱟᱹᱣ ᱠᱚ ᱚᱞᱟ, ᱡᱚᱛᱚ ᱟᱫᱷᱟ ᱜᱷᱟᱸᱴᱟ ᱨᱮ ᱰᱤᱞᱟᱨ ᱠᱚ ᱵᱚᱫᱚᱞᱟ, ᱵᱚᱞ ᱮᱰᱮ ᱵᱚᱫᱚᱞ ᱞᱟᱹᱜᱤᱫ ᱠᱚ ᱞᱟᱹᱭ ᱠᱚᱣᱟ, ᱟᱨ ᱮᱰᱮ ᱞᱟᱦᱟ ᱨᱮ ᱵᱟᱡᱤ ᱠᱚ ᱵᱚᱸᱫᱟ᱾ ᱞᱟᱵᱷ ᱟᱨ ᱵᱷᱟᱣᱛᱤᱠ ᱵᱤᱜᱭᱟᱱ ᱵᱟᱨᱭᱟ ᱢᱤᱫ ᱥᱮᱫ ᱜᱮ ᱩᱫᱩᱜᱟ᱾

ᱢᱤᱫ ᱥᱴᱩᱰᱤᱭᱳ ᱨᱮ ᱪᱟᱠᱟ ᱴᱷᱮᱱ ᱰᱤᱞᱟᱨ ᱠᱚ ᱵᱚᱫᱚᱞᱚᱜ-ᱟ ᱟᱨ ᱢᱤᱫ ᱥᱩᱯᱚᱨᱵᱷᱟᱭᱤᱡᱚᱨ ᱡᱚᱛᱚ ᱜᱷᱩᱨᱟᱹᱣ ᱮ ᱚᱞᱟ᱾

ᱥᱟᱹᱨᱤ ᱨᱮᱭᱟᱜ ᱦᱩᱰᱤᱧ ᱦᱟᱹᱴᱤᱧ ᱚᱠᱟ ᱨᱮ

ᱰᱤᱞᱟᱨ-ᱪᱤᱱᱦᱟᱹ ᱚᱱᱩᱢᱟᱱ: ᱡᱩᱫᱤ ᱟᱹᱰᱤ ᱢᱤᱫ ᱞᱮᱠᱟᱱ ᱛᱟᱦᱮᱸᱱ ᱠᱷᱟᱱ, ᱢᱤᱫ ᱰᱤᱞᱟᱨ ᱫᱚ ᱮᱰᱮ ᱴᱷᱟᱶ ᱛᱩᱞᱟᱹ ᱢᱤᱫ ᱥᱮᱠᱴᱚᱨ ᱥᱮᱫ ᱤᱴᱤᱡ ᱥᱴᱮᱴᱤᱥᱴᱤᱠᱟᱞ ᱞᱟᱸᱫᱷᱟ ᱮ ᱵᱮᱱᱟᱣ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ᱾ ᱪᱷᱟᱯᱟ ᱟᱠᱟᱱ ᱰᱟᱴᱟ ᱫᱚ ᱱᱚᱶᱟ ᱵᱟᱝ ᱩᱫᱩᱜ ᱟᱠᱟᱫᱟ; ᱥᱴᱩᱰᱤᱭᱳ ᱠᱚ ᱫᱚ ᱱᱚᱶᱟ ᱢᱮᱱᱟᱜ ᱫᱟᱲᱮᱭᱟᱜ ᱞᱮᱠᱟ ᱠᱚ ᱠᱟᱹᱢᱤᱭᱟ᱾ ᱟᱨᱱᱚᱞᱰ ᱥᱱᱟᱭᱰᱟᱨ (Arnold Snyder) ᱨᱮᱭᱟᱜ ᱜᱟᱞᱚᱪ ᱫᱚ ᱠᱩᱞᱤ ᱡᱷᱤᱡᱽ ᱜᱮ ᱵᱟᱹᱜᱤᱭᱟ: ᱪᱟᱠᱟ ᱚᱠᱛᱚ ᱚᱞᱤᱭᱟᱹ ᱥᱟᱶ ᱠᱟᱹᱢᱤᱭᱟᱹ ᱰᱤᱞᱟᱨ ᱫᱚ ᱜᱚᱲᱚ ᱞᱟᱹᱜᱤᱫ ᱜᱷᱩᱨᱟᱹᱣ ᱞᱟᱦᱟᱜ ᱮ ᱵᱮᱱᱟᱣ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ, ᱟᱨ ᱟᱹᱰᱤ ᱪᱟᱹᱞᱟᱠ ᱰᱤᱞᱟᱨ ᱫᱚ ᱢᱤᱫ ᱥᱮᱠᱴᱚᱨ ᱥᱮᱫ ᱫᱷᱚᱞᱟᱣ ᱮ ᱮᱢ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ᱾ ᱢᱤᱫ ᱥᱮᱠᱴᱚᱨ, ᱠᱷᱟᱱᱟ ᱫᱚ ᱵᱟᱝ᱾ ᱡᱩᱫᱤ ᱦᱩᱭ ᱦᱚᱸ ᱞᱮᱱ ᱠᱷᱟᱱ, ᱚᱱᱟ ᱫᱚ ᱥᱮᱠᱴᱚᱨ ᱵᱟᱡᱤ ᱨᱮ ᱛᱤᱱᱟᱹᱜ ᱜᱟᱱ ᱯᱟᱨᱥᱮᱱᱴ ᱵᱟᱲᱛᱤ ᱛᱟᱦᱮᱸᱱᱟ, ᱢᱤᱫ ᱜᱷᱩᱨᱟᱹᱣ ᱨᱮ ᱵᱟᱝ ᱧᱮᱞᱚᱜ, ᱟᱨ ᱠᱷᱮᱞᱚᱸᱰ ᱞᱟᱹᱜᱤᱫ ᱧᱟᱢᱚᱜ, ᱠᱮᱥᱤᱱᱳ ᱞᱟᱹᱜᱤᱫ ᱫᱚ ᱵᱟᱝ᱾

ᱠᱩᱠᱞᱤ ᱠᱚ

ᱤᱧᱟᱜ ᱰᱤᱞᱟᱨ ᱢᱤᱫ ᱜᱷᱟᱸᱴᱟ ᱨᱮ ᱯᱮ ᱫᱷᱟᱣ ᱡᱤᱨᱚ ᱮ ᱧᱟᱢ ᱞᱮᱫᱟ᱾ ᱥᱚᱱᱫᱮᱦ ᱨᱮᱭᱟᱜ ᱠᱟᱱᱟ ᱥᱮ?

ᱡᱤᱨᱚ ᱫᱚ ᱡᱚᱛᱚ ᱜᱷᱩᱨᱟᱹᱣ ᱨᱮ 1/37᱾ 50 ᱜᱷᱩᱨᱟᱹᱣ ᱨᱮ ᱯᱮ ᱫᱷᱟᱣ ᱫᱚ ᱡᱷᱚᱛᱚ 15% ᱚᱠᱛᱚ ᱨᱮ ᱦᱩᱭᱩᱜ-ᱟ᱾

ᱢᱤᱫ ᱠᱨᱩᱯᱤᱭᱮ ᱫᱚ ᱨᱚᱡᱽ ᱦᱮᱡᱚᱜ ᱠᱷᱮᱞᱚᱸᱰ ᱞᱟᱹᱜᱤᱫ ᱨᱩᱞᱮᱴ ᱨᱮ ᱵᱮᱭᱟᱹᱢᱟᱱᱤ ᱮ ᱠᱟᱹᱢᱤ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ ᱥᱮ?

ᱱᱤᱥᱟᱹᱱᱟ ᱛᱮ ᱫᱚ ᱵᱟᱝ᱾ ᱦᱩᱭ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ ᱠᱷᱟᱨᱟᱯ ᱵᱷᱩᱜᱛᱟᱱᱴᱟᱠᱟᱵᱷᱩᱜᱛᱟᱱᱡᱤᱛ ᱵᱟᱡᱤ ᱪᱮᱫ ᱨᱩᱣᱟᱹᱲᱟ, ᱨᱩᱣᱟᱹᱲ ᱵᱟᱡᱤ ᱴᱟᱠᱟ ᱪᱮᱛᱟᱱ “X ᱨᱮ 1” ᱞᱮᱠᱟ ᱨᱚᱲᱚᱜ-ᱟ᱾ᱯᱩᱨᱟᱹ ᱯᱟᱲᱦᱟᱣ → ᱛᱮ, ᱚᱱᱟᱛᱮ ᱜᱮ ᱠᱮᱢᱮᱨᱟ ᱟᱨ ᱵᱟᱨ ᱫᱷᱟᱣ ᱯᱟᱲᱦᱟᱣ ᱢᱮᱱᱟᱜ-ᱟ᱾

ᱰᱤᱞᱟᱨ ᱠᱚ ᱢᱩᱪᱟᱹᱫ ᱡᱤᱛ ᱮᱞ ᱠᱷᱚᱱ ᱪᱮᱫᱟᱜ ᱵᱚᱞ ᱠᱚ ᱮᱰᱮᱭᱟ?

ᱢᱤᱫ ᱛᱷᱤᱨ, ᱡᱟᱸᱪ ᱫᱟᱲᱮᱭᱟᱜ ᱱᱤᱭᱚᱢ᱾ ᱱᱚᱶᱟ ᱨᱮᱭᱟᱜ ᱢᱟᱱᱮ ᱦᱚᱸ ᱠᱟᱱᱟ ᱡᱮ ᱮᱰᱮ ᱴᱷᱟᱶ ᱫᱚ ᱡᱟᱦᱟᱱ ᱞᱮᱠᱟᱱ ᱜᱮ, ᱚᱱᱟᱛᱮ ᱰᱤᱞᱟᱨ ᱥᱟᱢᱵᱷᱟᱞᱚᱜ ᱢᱤᱫ ᱡᱤᱱᱤᱥ ᱠᱚᱢ᱾

ᱞᱟᱸᱫᱟ ᱞᱟᱹᱜᱤᱫ ᱠᱚᱢᱤᱠ

ᱵᱚᱞ ᱮᱰᱮ ᱟᱨ ᱨᱚᱴᱚᱨ ᱫᱚ ᱰᱤᱞᱟᱨ ᱨᱮᱭᱟᱜ᱾ ᱰᱤᱯᱷᱞᱮᱠᱴᱚᱨ ᱫᱚ ᱡᱟᱦᱟᱸᱭ ᱨᱮᱭᱟᱜ ᱦᱚᱸ ᱵᱟᱝ᱾

ᱵᱚᱞ ᱮᱰᱮ ᱟᱨ ᱨᱚᱴᱚᱨ ᱫᱚ ᱰᱤᱞᱟᱨ ᱨᱮᱭᱟᱜ᱾ ᱰᱤᱯᱷᱞᱮᱠᱴᱚᱨ ᱫᱚ ᱡᱟᱦᱟᱸᱭ ᱨᱮᱭᱟᱜ ᱦᱚᱸ ᱵᱟᱝ᱾

ᱯᱷᱮᱰᱟᱛ

  1. Small, M. & Tse, C. K. “Predicting the outcome of roulette.” Chaos 22(3), 2012.
  2. Barnhart, R. T. Beating the Wheel. Lyle Stuart, 1992.
  3. UK Gambling Commission. Remote gambling and software technical standards. ᱱᱤᱛᱚᱜᱟᱜ ᱥᱚᱝᱥᱠᱚᱨᱚᱬ᱾
  4. Wizard of Odds. “Roulette Basics”, ᱵᱚᱞ ᱪᱟᱞᱟᱣ ᱨᱮᱭᱟᱜ ᱦᱟᱹᱴᱤᱧ᱾ ᱟᱜᱚᱥᱴ 3, 2026 ᱨᱮ ᱱᱟᱣᱟ ᱟᱠᱟᱱ᱾
2026-09-28 ᱨᱮ ᱡᱟᱸᱪ ᱟᱠᱟᱱᱟᱢᱚᱰᱮᱞ: ᱭᱩᱨᱚᱯᱤᱭᱟᱱ, ᱥᱤᱝᱜᱚᱞ ᱡᱤᱨᱚᱦᱤᱥᱟᱹᱵ ᱦᱚᱨᱟ ↗ᱯᱷᱮᱰᱟᱛ ↗