Examining effects of sample concentration on estimates of live phytoplankton abundance
Bibliographic record
Abstract
With the entry into force of the International Maritime Organization's International Ballast Water Management Convention in September 2017, ships have begun to install and operate onboard ballast water management systems (BWMS) to reduce the number of live organisms in ballast water. Scientific methods were developed to assess the effectiveness of BWMS at reducing the number of live organisms in ballast water. However, detecting low organism concentrations in treated ballast water is challenging when considering the small sample volume (6 mL) analyzed for organisms in the 10–50 μm size class. The volume analyzed can be increased by concentrating the sample prior to analysis, but it is important to assess the effects of the sample concentration method due to potential cell loss experienced during the concentration step. Therefore, laboratory experiments were conducted to assess the effects of a gravity filtration method to concentrate samples to a factor of 40:1. Experiments were conducted for both low and high organism abundances. For unpreserved samples at low organism abundances (~10 cells mL−1), concentrated samples had on average 31% fewer live cells mL−1 than unconcentrated samples for four out of five experiments. At high organism abundances (≥ 120 cells mL−1), unpreserved concentrated samples had on average 55% fewer live cells than unconcentrated samples. Alternatively, with preserved samples at low organism abundances, concentrated samples had on average 4.5× more cells than unconcentrated samples. At high organism abundances, concentrated samples had on average 6.4× more cells than unconcentrated samples. Differences were also observed between preserved and unpreserved samples. These findings can help to improve ballast water monitoring procedures and BWMS assessments, addressing a critical challenge to maritime environmental protection.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".