Time-Frequency analysis of long-term marine data from Cambridge Bay in the Canadian Arctic. High resolution ADCP, CTD and Fluorometer time series analysis
Bibliographic record
Abstract
In the marine environment, the recorded time series are often nonlinear and nonstationary and interact with each other. Their analysis faces new challenges and thus requires the implementation of adequate and specific methods. We use the Hilbert-Huang Transform (HHT) for the spectral analysis of high frequency sampled time series in near shore waters of Cambridge Bay, located in the Canadian Arctic. We focus particularly on automatic measurements of temperature records, salinity, turbidity and chlorophyll data sets from deployments on an Ocean Networks Canada cabled platform. We look at the contribution of different Intrinsic Mode Functions (IMFs) \nobtained by the Empirical Mode Decomposition (EMD). The inertial wave and several low-frequency tidal waves are identified by the application of EMD. Furthermore, the correlation between two nonstationary time series is investigated. By Time Dependent Intrinsic Correlation (TDIC) analysis, it was concluded that the high-frequency modes have small correlation; whereas the trends are perfectly correlated. The methodologies presented in this paper are general. They can be applied for identification of main properties of other time series from the environmental and oceanic sciences, where the records are complex with fluctuations over a large range of different spatial and temporal scales.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.027 | 0.018 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".