Stable patterns in species distributions in metal-contaminated environments
Bibliographic record
Abstract
A large database of river diatoms (comprising more than 580 taxa) was constituted based on field surveys carried out in 6 different countries (France, Spain, Switzerland, Canada, Vietnam, China), in rivers exposed to various loads of heavy metals in the water. After taxonomy harmonization, the patterns in diatom community structure were investigated for 187 samples, all collected from hard substrates in rivers of circumneutral waters. As the sites were contaminated by a mixture of different metals (mainly Al, As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, Zn) with various loads, metal concentrations were converted into a single score after Clements et al. (2000) in order to determine 4 categories of metal inputs: background, low, moderate and high.\nThe biotypology (i.e. structuration of the diatom dataset) indicates that the species are influenced by the biogeographical context as well as metal inputs. The most structuring environmental parameters are investigated, and discriminating analyses are used to determine the relevance of some particular species (e.g. Eolimna minima, Nitzschia palea, Surirella angusta) as well as teratological forms for the biomonitoring of heavy metal pollutions. Special attention is also given to the information brought by other traits often cited as reliable for metal assessment, i.e. cell size distribution and diatom growth forms and postures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".