Bibliographic record
Abstract
Paleolimnological techniques have been used successfully to reconstruct environmental change in the Arctic and Antarctic. Diatoms are powerful indicators of environmental change because their community composition responds to changes in environmental conditions. As more regional diatom calibrations throughout the high latitude regions are achieved, the autecology of diatom taxa can be quantified and transfer functions for the driving environmental variables developed. In most instances, environmental variables related to physical, chemical, and climate-related characteristics are the main drivers affecting diatom distribution across polar aquatic bodies. A decline in ice cover and increase in growing season length results in an increase in diatom diversity as well as increased productivity, and increased thermal stratification in lakes (vs. shallow ponds). Because the siliceous cell wall preserves well in sediments, diatoms are among the most commonly used organisms used in paleolimnological analyses. Polar latitudes are experiencing amplification of the current global warming trend and as such, analyses of diatoms from high latitude lake and pond sediments are revealing the timing and extent of these trends. Diatom-based paleolimnological analyses are also being used to track the environmental impact of excess nutrient additions to lakes. Similar findings have also been reported from marine ecosystems.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".