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Record W2113286023 · doi:10.1680/jees.2013.0017

Interpreting nonstationary environmental cycles as amplitude-modulated (AM) signals

2013· article· en· W2113286023 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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2013
Typearticle
Languageen
FieldMathematics
TopicStatistical and numerical algorithms
Canadian institutionsUniversity of British ColumbiaBC Hydro (Canada)
FundersMinistry of Education, IndiaMinistry of EnvironmentMinistry of Earth SciencesHealth CanadaDistrict of Columbia Department of Health
KeywordsAmplitudeAmplitude modulationEnvelope (radar)Diel vertical migrationModulation (music)RhythmNonlinear systemPerspective (graphical)Annual cycleEnvironmental scienceSeries (stratigraphy)SIGNAL (programming language)Computer scienceFrequency modulationBiological systemPhysicsTelecommunicationsClimatologyEcologyRadio frequencyAcousticsGeologyRadarBiologyArtificial intelligenceOptics

Abstract

fetched live from OpenAlex

Inspired by an analogy to AM radio signals, amplitude modulation (AM) is proposed here as a useful view of nonstationary environmental periodicities, and applied to hydrologic and air quality datasets. Both example time series considered exhibit seasonally evolving diel cycles, with large (small) daily cycle amplitudes in summer (winter). The carrier wave is taken to be a sinusoidal daily cycle; this is multiplied by an information signal consisting of a sinusoidal annual cycle, forming an envelope to the diel variations. Our results suggest that amplitude modulation may offer a novel, compact, and accessible perspective, both qualitatively and quantitatively, on the net phenomenological behaviour arising from highly complex, nonlinear, and diverse environmental process dynamics. Physical interpretations, synergies with common environmental time series processing or analysis methods (Kolmogorov-Zurbenko filtering, classical spectral analysis, and singular systems analysis), and potential future research directions are also explored.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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