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Dendrogeomorphic Potential of the Himalaya – Case Studies of Process Dating of Natural Hazards in Kullu Valley, Himachal Pradesh

2017· article· en· W2793338246 on OpenAlexaff
Amalava Bhattacharyya, Markus Stoffel, Mayank Shekhar, Juan Antonio Ballesteros‐Cánovas, Daniel Trappmann

Bibliographic record

VenueCurrent Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsImpact
Fundersnot available
KeywordsLandslideFlash floodNatural hazardRockfallContext (archaeology)Natural (archaeology)Physical geographyGeographyGeologyDendrochronologyClimate changeSnowNatural disasterDebris flowDebrisArchaeologyGeomorphologyFlood mythMeteorologyOceanography

Abstract

fetched live from OpenAlex

Trees impacted by the forces of natural processes such as flash floods, snow avalanches, landslides, rockfalls or earthquakes, record these events and exhibit growth disturbances in their growth-ring series. As a consequence, these disturbances provide an excellent signal for the spatio-temporal reconstruction of past natural hazard activity and a means to date and document past disasters. In the context of the Indian Himalayas Climate Change Adaptation Programme (IHCAP; http://www.ihcap.in/), a field trip was carried out in May 2014 to define suitable sites for dendrogeomorphic research in Kullu valley, Himachal Pradesh. Several tree species and sites where recent and past process activity can be reconstructed were inventoried, namely flash floods in the Beas and Sainj rivers as well as snow avalanches in Solang valley. Through this exploratory analysis, we ascertain that tree-ring techniques have wide applicability in the analysis of natural hazards, not only in the Kullu region but also in other geographical contexts of the Himalayas.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.329
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2017
Admission routes1
Has abstractyes

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