Reconciling Coulter Counter and laser diffraction particle size analysis for aquaculture monitoring
Bibliographic record
Abstract
The disaggregated inorganic grain size (DIGS) of bottom sediment analyzed with a Coulter Counter (CC) has been used to show that the fraction of sediment deposited in flocs (floc fraction) increased in both the near and far field after the introduction of open cage salmon aquaculture, altering benthic habitat and species composition. As a result, DIGS was identified as a potential indicator of regional environmental changes due to aquaculture. Laser diffraction is an attractive alternative to the CC because of its greater efficiency and larger size range. To determine if a laser diffraction instrument, Beckman-Coulter LS 13 320 (LS), could replace the CC within a Canadian national aquaculture monitoring program, the DIGS of 581 samples from five different regions in eastern Canada were analyzed with an LS and a CC. Results show that the LS could not be used to calculate floc fraction. Instead, % sortable silt and the volume % of inorganic particles < 16 µm were evaluated as alternative proxies for fine sediment properties. LS and CC values for these parameters were correlated, but they were significantly different and the relationship between the instruments was dependent on the area sampled. The LS did not capture variations between areas seen in the CC DIGS data. Where the DIGS from the CC found no sorting in the finest size classes, all the LS samples had similar size distributions characterized by smooth modal peaks. The LS and CC both return values that can be used to monitor changes in the deposition of fine-grained sediment, but the LS cannot determine changes in floc deposition and caution is required if comparing different sedimentary environments.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".