Taxon selection using statistical learning techniques to improve transfer function prediction
Bibliographic record
Abstract
Transfer functions are widely used in palaeoecology to provide quantitative environmental reconstructions using biological proxies. Most models use all but the rarest taxa present in the training set, even though many may be unrelated to the environmental variable of interest. We hypothesise that retaining such non-informative taxa will reduce model robustness and present a method for variable selection motivated by the statistical learning algorithm in random forests. We apply our species-pruning algorithm into weighted averaging (WA) and maximum likelihood calibration of response curves (MLRCs), and compare results of boosted regression trees (BRTs) using artificial and real datasets. Results from the artificial data show that WA is particularly sensitive to the influence of both non-informative taxa and secondary environmental variables in the training set or fossil assemblage, and that BRTs are relatively immune to these effects. Furthermore, species-pruned WA and MLRCs offer substantial improvements over all-species models when the training set includes non-informative taxa but does not guard against confounding effects when species have bi- or multivariate responses to the primary and one or more secondary variables. Tests with a limited set of examples of real data indicate that BRTs, MLRCs or species-pruned models have no apparent advantage over WA. We discuss possible reasons for this contradiction and suggest that more tests are needed to properly evaluate BRTs and species-pruned models.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".