Collapse of terrestrial ecosystems linked to heavy metal poisoning during the Toarcian oceanic anoxic event
Bibliographic record
Abstract
Abstract The Early Jurassic Toarcian oceanic anoxic event (T-OAE, ca. 183 Ma) was accompanied by a major biotic turnover in the oceans and substantial vegetation change on land. The marine biotic crisis has been attributed to several triggers, e.g., anoxia, warming, ocean acidification, yet the processes underlying the collapse of the terrestrial ecosystem are poorly understood. New high-resolution geochemical and palynological data across the T-OAE from a lacustrine succession in North China reveal elevated occurrences of spore dwarfism, asymmetrical Classopollis tetrads, and aberrant spores coeval with increases in heavy metal (Hg, Cu, Cr, Cd, Pb, As) abundances. The occurrence of teratological spores and pollen in multiple plant groups suggests overall vegetation-scale ecological pressure. Our data indicate that the combination of a widespread floral crisis with higher terrestrial organic matter oxidation and decomposition, enhanced hydrological cycle, and coeval large-scale volcanism resulted in higher concentrations of toxic heavy metals in terrestrial ecosystems. These heavy metals could poison plants, causing mutations and disrupting their reproductive cycle, and making them more vulnerable to secondary stresses such as climatic extremes and/or habitat shifts, eventually leading to widespread collapse across all terrestrial trophic levels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".