Bibliographic record
Abstract
Adaptive radar signal processing, 2 Adaptive reconstructed spectrum, 53 with prewhitening, 55 Adaptive spectra, [28][29][30][31] 34.See also Adaptive reconstructed spectrum Amplitude distribution, 126-127.See also Clutter amplitude entries Amplitude modulation (AM), 144-145.See also Hybrid AM/FM sea clutter model Amplitude spikes, 124.See also Spikes Analysis of variance (ANOVA), 36 Angle(s) of arrival, 5, 18 Angle-of-arrival (AOA) estimation, xi, 11-89.See also Estimated AOAs diffuse multipath spectrum estimation, 78-84 F-tests used for, 88 low-angle tracking radar study, 60-63 MTM and ML methods in, 86 multi-taper spectrum estimation, 28-35 spectrum estimation procedures, 23-27 Angular separation, between direct and specular components, 68 ANOVA table , 36t Antenna horn transmission frequencies, 61 AOA estimates, variability in, 85.See also Angle-of-arrival (AOA) estimation A priori probabilities, 205 AR bandwidth, 180.See also Autoregressive entries AR centroid, 180 AR coeffi cients, estimation of, 54 AR fi lter, for prewhitening, 54 AR model parameters, variation of, 180-181 Beamforming, 13-14 Bimodal frequency distributions, 145 Bistatic mode, 12 Blackman and Tukey spectrum estimate, 15 Bragg resonant length, 160 Bragg scattering, 124, 129, 160, 162 dominance of, 188 Bragg spectral lines, 160, 161 Breaking waves, 124-125 "Breathing" phenomenon in time-Doppler plots, 145 Brewster effect, 125 Broadband bias, 29, 30 C Capillary waves, 123, 124, 159 Cauchy-Schwartz inequality, 29 CFAR processor, 113.See also Doppler CFAR (constant false-alarm rate) receiver Chaos, 130 stochastic, 153-155 theory, 7, 122 Chaotic invariant analysis, as a selffulfi lling prophecy, 137-138 Chaotic invariants, of sea clutter, 132-134 Chi distribution, 126, 127, 128 Classical spectrum estimation method, 26 Classical statistical approach, versus nonlinear dynamical approach, 152-153 Clutter, unknown targets in, 218.See also Coherent radar clutter; Sea clutter entries Clutter amplitude, correlation properties of, 127-128 Clutter amplitude statistical models, 152 Clutter Doppler frequency, 211-213 Clutter modeling, goals related to, 179 Clutter power (P clutter ), 199, 200 Clutter power spectral density (PSD), 174, 175 Clutter statistics, 205 Clutter-to-noise power ratio (CNR), 175 CM-RELAX algorithm, 187-188, 189 Coherence, 4 Coherent radar clutter, modeling, 151 Coherent radars, 123 Coherent sidelobe subtraction, 98 Complex observed signal, 206 Composite reconstructed spectrum, with prewhitening, 56.See also Composite spectrum Composite spectrum, 34 estimation, 32-33, 52-54 reconstruction, 53 Composite surface theory, 169 Compound-Gaussian model, 162.See also Gaussian model Compound K-distribution, 126-128 model, 127 Continuous colored noise component, leakage of, 52-54 Continuous part of the spectrum (second moment), 17 Correlation, multi-taper estimate of, 97 Correlation anomaly algorithm, 195 Correlation anomaly detection strategy, 205-206 Correlation anomaly receiver, 205-217, 218 comparison with Bayesian direct fi lter, 206-217 Correlation dimension, 132, 133 Covariance functions, 94-95.See also Cross-covariance functions (CCF) Cramér-Rao bounds, 38, 39 Cramér representation, 94 Cross-covariance functions (CCF), 175, 176, 178, 188.See also Covariance functions Crude spectra, 33 Currie, Brian, 119, 193 Cycle (cyclic) mean, 183, 184-185 estimating, 186, 187 of intensity data, 186-187 Cycle frequency, 184 Cyclic statistics, 183-185 Cyclostationary (CS) processes, 183 fi rst-order, 183, 185 Cyclostationarity property, 105-106, 112 of sea clutter, 189 D Dartmouth database , 195, 197, 200 Data, multiple snapshots of, 57 Data-adaptive parameter estimation, 13 Data and spectral windows, procedure for computing, 22 10.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.726 | 0.651 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".