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Transition of Land Cover Characteristics at Wild-Fired Watershed

2014· article· en· W2051649037 on OpenAlexfundno aff
Kwang Ik Son, Gyo Chang Son, Hyung Joon Kim, Jung Ho Kim

Bibliographic record

VenueAdvanced materials research · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEcology and Conservation Studies
Canadian institutionsnot available
FundersInstitute of Musculoskeletal Health and ArthritisMinistry of Land, Infrastructure and Transport
KeywordsWatershedSurface runoffEnvironmental scienceHydrology (agriculture)Land coverErosionLand useSustainabilityWater resource managementEcologyGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Wild fires in a mountain area cause severe runoff. The runoff causes secondary mal-effects such as soil erosion and environmental contamination. Korea had suffered from serious soil yield problems at Imha reservoir in 2003. The muddy flow in the reservoir lasts for an years at that time and the water resources problem had prevailed around the watershed. But there was no reliable method in predicting the amount of soil yield and developing count measures against soil erosion. The goal of this research is to find the sustainability transition of land cover characteristics in a wild-fired watershed. For the success of this research, experimental watershed which had suffered from wild-fires was operated last five years. With the collected field data, the transition of land cover characteristics of watershed was analyzed. It was found that the land cover factor was increased about one hundred times at first year after the wild fire. Then it decreases constantly until it remains stable condition which is reached at fourth year after wild-fires.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.049
GPT teacher head0.299
Teacher spread0.249 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2014
Admission routes1
Has abstractyes

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