Femtosecond Stimulated Raman Spectroscopy for Detecting Inorganic Phosphate in the Great Lakes
Bibliographic record
Abstract
The eutrophication of rivers, lakes, and marine coastlines remains a persistent global issue. In Lake Erie, excessive nutrient inputs have led to the growth of harmful algal blooms (HABs) that cause fish to die off in large numbers, among other negative outcomes (Wurtsbaugh et al., 2019). HABs also lead to poor water quality, which is a concern for the millions of people who rely on the Great Lakes for drinking water and recreation (Sellner et al., 2003). For instance, certain species present in HABs contain toxins that can alter cellular processes of organisms from plankton to humans (Harper at al., 1992; Sellner et al., 2003). Soluble reactive phosphate (SRP) is a small subset of the total phosphorus (TP) of an aquatic environment and is usually in the form of orthophosphate. SRP is the most important component of TP for biological functions and is a key contributor to the growth of algae. There has been a recent increase in HAB growth in Lake Erie, and it has been proposed that, despite a reduction in the overall loading of TP to Lake Erie over time, the fraction of more bioavailable SRP may be increasing (Maccoux et al., 2016). Traditionally, SRP has been measured via an absorbance measurement in the UV-visible region of the electromagnetic spectrum. In the most common method, molybdate is used to form a blue-coloured complex with orthophosphate (Tarapchak, 1983). In this case, the concentration of SRP is inferred via the absorption of the molybdate-phosphate complex. Issues with this method include signal interference, lengthy measurement time, difficulties making in-situ measurements, and toxicity of reagents (Sarwar et al., 2019). There is a need for a new technique to measure phosphate in lakes, and we propose measuring the vibrational properties of the nutrient via its Raman response.
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".