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Record W4205286591 · doi:10.5351/kjas.2003.16.1.015

Exploratory Analysis of Bioindex Data : Based on a Data Set from take Ontario

2003· article· en· W4205286591 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueKorean Journal of Applied Statistics · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrological Forecasting Using AI
Canadian institutionsnot available
Fundersnot available
KeywordsCorrelogramVariogramData setExploratory data analysisConstruct (python library)Set (abstract data type)StatisticsComponent (thermodynamics)GeographyEconometricsComputer scienceMathematicsKriging

Abstract

fetched live from OpenAlex

Lake Ontario에서 수년간 측정된 실제 생물학적 지표 자료의 각 변수에 대하여 관찰시점의 불규칙성과 의존성을 고려한 탐색적 분석모형의 수립과정에 대하여 연구하였다. 이 상점을 제거한 후 trend와 seasonal component를 수정 한 선형 모형으로부터 잔차를 계산하고 이로부터 variogram과 correlogram을 그려보았다. In this study, we will construct a statistical model which considered the irregularity of observed time sequence in order to analyze sets of bioindex data gathered from stations in Lake Ontario for a number of years. We fit a linear model to account for the trend and seasonal component in an exploratory way and draw variogram and correlogram for further confirmatory studies.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.090
GPT teacher head0.279
Teacher spread0.188 · 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