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Record W4283160230 · doi:10.5194/icg2022-4

A general systems approach to mountain geomorphology

2022· preprint· en· W4283160230 on OpenAlexaff
Olav Slaymaker, Eric Leinberger

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnthropoceneNeglectClimate changeClimate systemRelation (database)Value (mathematics)Human systems engineeringSedimentGeographyGeomorphologyMathematical economicsComputer scienceEnvironmental ethicsGeologyMathematicsPhilosophyOceanography

Abstract

fetched live from OpenAlex

On this 60th. anniversary of Chorley’s paradigm changing paper on geomorphology and general systems theory, the application of his ideas specifically to mountain geomorphology is briefly reviewed. Five kinds of general systems are recognized: (i) morphological systems; (ii) spatio-temporal systems; (iii) water, solute and sediment cascading systems; (iv) mountain process-response systems; and (v) so-called mountain control systems that are actually “out-of-control” systems because of policy and planning failures following intensification of land use and climate change during the Anthropocene epoch. Two examples of research questions under systems categories (iii) and (v) illustrate the value of a general systems approach. The first question concerns the relation between disconnectivity and connectivity and discusses reasons for the comparative neglect of disconnectivity in the recent geomorphological literature. The second question concerns the geomorphological effects of human use of land and associated climate change and discusses reasons for the failure of many policy and planning analysts to recognize the links between human use of land, climate change and environmental degradation.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.014
GPT teacher head0.231
Teacher spread0.217 · 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 designTheoretical or conceptual
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

Citations0
Published2022
Admission routes1
Has abstractyes

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