Rapid Communication / Communication RapideAcoustic seabed classification: improved statistical method
Bibliographic record
Abstract
Huge amounts of money will be spent by industrialized nations during the next decades to obtain detailed maps of continental shelf seabeds. These maps, which will allow a more rational exploitation of the sea floor, are needed to assess the impact of anthropic activities. The statistical method of analysis of echosounder backscatter data described in this paper presents several improvements over existing techniques. The steps are as follows. (i) The backscatter data are decomposed mathematically into a number of quantitative variables, which are subjected to principal component analysis (PCA). (ii) Principal components representing 9599% of the variation are used in a K-means partitioning procedure. A statistical criterion indicates what the number of groups is that best reflects the variability of the data. (iii) The groups are then plotted on maps of the survey area. Insofar as the mathematical decomposition produces variables that reflect the variations of the physical nature and composition of the seabed, the classes of the partition will correspond to different seabed types. Free software (The Q Package) implementing this method is available at http://www.fas.umontreal.ca/biol/legendre/.
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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".