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Innovative Signal Utilization and Processing

2003· article· en· W2090904614 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMeteorological Monographs · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsMcGill University
Fundersnot available
KeywordsRadarSignal processingComputer scienceData processingRadar signal processingSIGNAL (programming language)State (computer science)Remote sensingReal-time computingTelecommunicationsGeologyAlgorithmDatabase

Abstract

fetched live from OpenAlex

The design and implementation of signal-processing algorithms are specialized trades of radar meteorology practiced by a small group of experts and poorly understood by most other radar data users. Yet signal processing is the essential first step of radar data processing, and the skill with which it is done determines the type and quality of data that will be available to radar meteorologists. Like many other facets of radar meteorology, it is undergoing a rapid evolution as computing capabilities expand exponentially. In this chapter, an overview of the current state and evolution of signal processing for the nonspecialist is provided. To achieve this, the nature and the properties of the radar signal itself is first described, as it determines the type and quality of the information that can be obtained. After these foundations are laid, the current state of signal processing on operational radars and then some of the latest developments that may shape the future are described.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.301

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 head0.031
GPT teacher head0.251
Teacher spread0.220 · 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