Identifying optimal sets of ecosystem indicators: A comparative study of data analysis methods and regional results
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.We are investigating quantitative approaches for identifying optimal sets of ecosystem indicators andcomparing our findings for different regions. 'Optimal' is defined as indicator sets that best predictstakeholder-defined ecosystem state with the least data requirements. Here we present our analysis usingdata from 1985 – 2013 for the Grand Banks off Eastern Canada. Time series of dozens of indicators werecalculated and categorized as ecosystem drivers, pressures or states.Correlations within each categorywere used to identify and justify the removal of redundant indicators. The remaining indicators werethen combined to predict ecosystem state indicators using multivariate multiple regression, and optimalpredictor sets were identified from the results. We discuss these findings and outline our future plans toexplore neural network analysis and compare results for Georges Bank, which straddles the US andCanadian borders in the Northwest Atlantic.
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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 | 0.237 | 0.381 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".