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Record W33767608 · doi:10.3389/fnins.2021.626466

The impact of monetary policy on commercial bank lending in Malaysia: The investigation on agriculture, construction & manufacturing sectors

2005· article· en· W33767608 on OpenAlexfundno aff
Mohd Amy Azhar Mohd Harif, Mohd Zaini Abd Karim, Azira Abdul Adzis

Bibliographic record

VenueFrontiers in neuroscience · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMonetary policyLoanAgricultureEconomicsBank rateMonetary baseVector autoregressionBusinessFinancial systemMonetary economicsFinanceCentral bank

Abstract

fetched live from OpenAlex

The Campbell-Robson chart is a highly popular figure used in psychophysics and visual perception textbooks to illustrate the Contrast Sensitivity Function (CSF). The chart depicts a grating which varies logarithmically in spatial frequency (SF) from left to right and in contrast from bottom to top. Campbell and Robson's (1964) intuition was that the boundary between the grating and the homogeneous gray area (below threshold) would trace the shape of the observer's own CSF. In this paper, we tested this intuition. A total of 170 participants (96 adults and 74 children) adjusted the four parameters of a truncated log-parabola directly onto a Campbell-Robson chart rendition and completed a gold-standard CSF evaluation. We hoped that this procedure which requires a mere three clicks on the computer mouse, would speed up the measurement of the CSF to under a minute. Unfortunately, the only parameter of the truncated log-parabola fitted to the gold-standard CSF data that could be predicted from the Campbell-Robson chart data was the peak sensitivity for the adult participants. We conclude that the curve visible on the Campbell-Robson chart cannot be used practically to measure the CSF.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.225
Teacher spread0.212 · 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 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

Citations0
Published2005
Admission routes1
Has abstractyes

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