RELATIONSHIP BETWEEN GRAIN SIZE AND HEAVY METALS IN SEDIMENTS FROM BEACHES ALONG THE COAST OF GUYANA
Bibliographic record
Abstract
This paper examined surficial sediments collected from eroding and accreting beaches along the coast of Guyana for concentrations of toxic heavy metals. Twenty-four samples were collected, twelve from each of the eroding Melanie Beach and the accreting Albion Beach. Three grain size fractions (4.0 phi, 5.0 phi and >5.0 phi) plus twenty-four bulk samples less than 4.0 phi in diameter were analyzed for a total of 96 samples. Each sample was examined for the presence of eight heavy metals, these being aluminum, chromium, copper, iron, lead, nickel, vanadium and zinc. The samples were chemically analyzed by aquaregia, followed by inductively coupled plasma-optical emission spectroscopy. Discriminant analysis, analysis of variance and correlation and regression techniques were used to analyze the datasets. Results from the discriminant analysis emphasized that heavy metal concentrations were unique to each beach. The analysis of variance (ANOVA) showed that grain size of the sediment had a pronounced effect on the concentrations and spatial distribution of heavy metals. Correlation and regression analysis substantiated the ANOVA results, and revealed the existence of an inverse relationship between the concentrations of heavy metals and the grain size of sediments. The Albion accretionary beach, with finer sediments, accumulated far more heavy metals than the coarser-grained receding Melanie Beach. At the Albion Beach the concentrations of all heavy metals increased in a shoreward direction while at the Melanie Beach the concentrations of all heavy metals decreased in a shoreward direction.
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 | 0.000 |
| 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 | 0.001 | 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 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".