Prelims-Pinpointer-for-10-September-2025

Why in News: The invasive Giant African Snail (Lissachatina fulica) has recently been detected in Chennai and its outskirts, posing ecological, agricultural, and public health risks.

About Giant African Snail (GAS):

  • Scientific name – Lissachatina fulica
  • Among the world’s 100 worst invasive species (IUCN).
  • Native to East Africa; introduced to India in 1847.
  • Reported in several states: Tamil Nadu, Kerala, Karnataka.

Spread in Chennai (2024):

  • Detected in St Thomas Mount, Tirusulam, Perungalathur Hills.
  • Thrives in urban/semi-urban habitats.
  • Flooding during monsoon accelerates dispersal.

Health Risks:

Carrier of parasitic nematodes:

  • Angiostrongylus cantonensis → eosinophilic meningoencephalitis (brain inflammation).
  • Angiostrongylus costaricensis → abdominal angiostrongyliasis.
  • Infection route – ingestion of contaminated snails/residues.

Ecological & Agricultural Impact:

  • Feeds on 500+ plant species, including crops.
  • Competes with native snails; spreads plant pathogens.
  • Causes severe crop loss & ecosystem damage.

Global Distribution:

  • Widely spread in Asia, Africa, Americas, Pacific islands.
  • Adaptable to multiple habitats → difficult to eradicate.

Control & Management:

  • Rapid reproduction, flood-assisted dispersal → eradication challenging.

Measures:

  • Public awareness & community monitoring.
  • Integrated pest management.
  • Strict regulations in some countries banning possession/sale.

Significance:

  • Highlights biosecurity risks of invasive species.
  • Chennai’s case underlines need for early detection & coordinated response to biological invasions.

Why in News: India will conduct its first fully digital Census in 2027, with enumerators using their own smartphones and geo-tagging all buildings.

Key Features of Census 2027:

  • First fully digital Census – no paper-based enumeration.
  • Enumerators: ~34 lakh, using personal smartphones (Android & iOS).
  • Mobile Applications: Multi-language support, upgraded from 2021 design.
  • Direct Upload: Data sent to a central server → faster & accurate processing.
  • Fallback Option: Paper data (if collected) → entered later into web portal.

Phases of Census:

  • Houselisting operation: April – September 2026.

Population enumeration:

  • Feb 2027 (most states).
  • Sept 2026 (Ladakh, J&K, Himachal Pradesh, Uttarakhand).
  • Self-enumeration option via web portal.

Geo-Tagging & Digital Mapping:

  • All buildings (residential & non-residential) to be geo-tagged.
  • Digital Layout Mapping (DLM): Unique latitude-longitude assigned to each building.
  • Linked to Houselisting Blocks (HLBs) on GIS map → higher spatial accuracy.

Monitoring & Budget:

  • Real-time monitoring website under Registrar General of India (RGI), MHA.
  • Budget allocation: ₹14,618.95 crore.
  • Comparison: 2011 Socio-Economic Caste Census used BEL-supplied devices; now personal devices + self-enumeration.

Significance:

  • Ensures speed, transparency & accuracy in data collection.
  • Enhances GIS-based planning & policy-making.
  • Promotes citizen participation through self-enumeration.

Why in News: The Union Ministry of Culture will host an international conference (Sept 11–13, 2024, New Delhi) on deciphering the Harappan script.PM Narendra Modi to attend on Sept 12; Union Home Minister Amit Shah on Sept 13.

Harappan Script – Key Facts:

  • Discovered in Harappa & Mohenjo-daro (1920s).
  • Over 4,000 inscriptions found on seals, tablets, pottery.
  • Script remains undeciphered → no bilingual inscription (like Rosetta Stone for Egyptian).
  • Symbols: ~400–600 signs (pictographic/ideographic).
  • Writing direction: Right-to-left, sometimes boustrophedon (alternate lines opposite).

Debates on Language:

  • Some scholars link it to Sanskrit.
  • Others → Proto-Dravidian language family.
  • Some suggest links to Munda languages (Santali, Gondi).
  • No consensus yet.

Decipherment Challenges:

  • Short length of inscriptions (average 5 signs).
  • No bilingual texts.
  • Limited context (mostly seals, not long manuscripts).

Static Info – Harappan Civilization (3300–1300 BCE):

  • Also called Indus Valley Civilization (IVC).
  • Mature Phase: 2600–1900 BCE.
  • Major sites: Harappa, Mohenjo-daro, Dholavira, Lothal, Rakhigarhi, Kalibangan.
  • Features: Urban planning (grid system, drainage), standard weights, seals, trade links (Mesopotamia).
  • Decline around 1900 BCE due to climate change, river shifts, declining trade.

IGNCA (Indira Gandhi National Centre for the Arts):

  • Autonomous institute under Ministry of Culture.
  • Established: 1987.
  • Functions: Research, documentation, dissemination of Indian arts & heritage.

Significance:

  • Harappan script = key to understanding social, economic, cultural life of IVC.
  • Decipherment would clarify origins of Indian languages and civilizational continuity.

Why in News:ISRO has begun conducting Gaganyaan Analog Experiments (Gyanex), where astronauts spend several days in spacecraft-like confined conditions to simulate human spaceflight missions.

Key Details

  • Objective: To prepare astronauts for space-like conditions and help ISRO develop protocols for communication, resource management, and crew activities.
  • Limitation: Unlike actual missions, gravity is present during these simulations.
  • Location: Conducted in a static mock-up spacecraft simulator in Bengaluru.

First Mission (Gyanex-1):

  • Held in July 2025.
  • Group Captain Angad Pratap and two others stayed confined for 10 days.
  • Conducted 11 scientific experiments.
  • Monitored parameters: crew activities, psychological impact of confinement, and routine management.
  • Future Plan: Series of Gyanex missions to refine protocols before the actual human spaceflight.

Associated Institutions

  • DRDO: Provides astronaut food.
  • Astronaut Training: Complementary training also in Russia and the US, including exposure to microgravity environments.

Static Info 

  • Gaganyaan Programme: India’s first human spaceflight mission by ISRO.
  • Mission Timeline: First crewed mission slated for 2027.
  • Crew Module: Will carry 2–3 astronauts to low Earth orbit (~400 km).
  • Launch Vehicle: HLVM-3 (Human-rated LVM3, previously GSLV Mk-III)

Why in News: Rapid advances in GPT-based and other AI chatbots, which can emulate human-like conversation and emotions, have sparked debates on whether they could ever become conscious.

Key Concepts

Consciousness:

  • Phenomenal consciousness – subjective first-person experience (“what it feels like”).
  • Access consciousness – ability to reflect and use knowledge deliberately.

Current AI Systems:

  • Operate on statistical pattern recognition.
  • Lack memory, beliefs, emotions, or subjective experience.
  • Produce output via input–output mappings, not true comprehension.

Arguments Against Chatbot Consciousness

1. No Subjective Experience – algorithms cannot feel pain, joy, or awareness.

2. No Intentionality – no independent goals or desires.

3. No Self-awareness – cannot truly reflect on existence.

4. No Embodiment – lack of bodily/sensorimotor interaction, crucial in some theories of consciousness.

Related Concept

  • ELIZA Effect: Human tendency to attribute emotions/agency to machines that merely simulate conversation.

Case Study

  • 2022: Google engineer Blake Lemoine claimed LaMDA chatbot was sentient → resulted in his dismissal.

Ethical Concerns

  • Over-trust & deception in sensitive domains (healthcare, law).
  • Emotional attachment risks → psychological harm or exploitation.
  • Bias & misinformation liability.
  • Job displacement due to growing chatbot capabilities.

Speculative Outlook

  • Some argue machine consciousness could arise if computational systems mimic brain processes.
  • Barriers remain: consciousness may rely on biological/quantum mechanisms unique to living beings.
  • Raises dilemmas of rights, personhood, and ethics if machine consciousness ever emerges.

Prelims Static Info

  • ELIZA chatbot (1960s): Early natural language program that mimicked conversation.
  • Large Language Models (LLMs): AI trained on vast text corpora to predict word patterns, not to “understand”.

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