Bibliographic record
Abstract
Using data recorded and made available by Network Canada, the fluctuations in the bottom high-frequency acoustic backscatter from a node in the Strait of Georgia was observed over a period of 11 days, along with other environmental factors, including hydrostatic pressure, temperature, and salinity. The bottom backscatter was observed using an Imaginex rotating sonar operating at a frequency of 1 MHz. The likely original purpose of the instrument was to observe bottom sediment pulses driven by tidal currents, but correlation between other environmental parameters and bottom backscatter are also evident. Due to the relatively shallow depth of 41 m, there should be sufficient sun light penetration for photosynthesis, which affects backscatter due to bubble generation by microorganisms in the sediment, as reported by Holliday et al. [J. Acoust. Soc. Am. 2003, 114, 2317]. Indeed, a small sun light correlation coefficient was found, with a periodicity of 1 day. Larger correlation coefficients were found with salinity and temperature variations, with longer periodicities. The temperature and salinity of the water were correlated, suggestive of the movement of two distinct bodies of water, in the manner of SPICE. [Work supported by the US Navy’s Office of Naval Research, Code 322OA, grant N00014-23-1-2522.]
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".