Diatom communities monitoring in temporary rivers of Sant Llorenc del Munt i l'Obac Natural Park (Catalonia, Spain)
Bibliographic record
Abstract
This dataset includes 30 sites with diatom counts and associated water chemistry parameters from temporary rivers in Sant Llorenc del Munt i l'Obac Natural Park (Catalonia, Spain). Diatom data represents epilythic samples collected at 4-5 different points of each of the seven rivers (Vall d'Horta, Rellinars, Santa Creu, Sanana, Talamanca, and Mura) during the seasons of 2019. These streams showed a wide degree of flow permanence, which allowed the recording of different typical phases of temporary rivers (moments of maximum water flow, cessation, disconnection, and drought). The dataset has been formatted following the standards of the Tropical South American Diatom Database (https://zenodo.org/records/5721364)—a database constituent of Neotoma (www.neotomadb.org), a global community-curated database by regional experts for multiple types of paleoecological data—and as part of the project "DiatomS mEEt Databases: resources and practices to enable large-scale ecological research (SEED)" funded by the International Society for Diatom Research (https://isdr.org/early-career-networking-award/).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".