Big Data in Biogeography: Crustacean Responses to Historical Environmental Changes
Bibliographic record
Abstract
This study summarizes the current status of crustacean biogeography research and explores the integration of big data resources (such as modern and historical distribution records, paleoclimate and paleoecological data, and molecular systematics information) and their application in spatial modeling and evolutionary biogeography research. The study found that historical climate change, sea level change, and continental drift significantly affected the global distribution pattern, community succession, and population dynamics of crustaceans; crustaceans in freshwater, marine, and island environments showed specific response mechanisms. At the same time, this study also pointed out the challenges of data sparsity, time scale asymmetry, data bias, and uncertainty assessment of multi-data fusion in current research. In the future, cross-disciplinary and multi-scale data integration and model optimization should be strengthened to more accurately predict the response of crustaceans to future environmental changes. This study emphasizes that the widespread application of big data has promoted biogeographic research from static to dynamic, from local to global perspectives, and deeply revealed the ecological adaptation and evolution mechanism of crustaceans, providing an important theoretical basis for biodiversity conservation and ecosystem management.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".