Peer Review #2 of "Morphological traits: predictable responses to macrohabitats across a 300 km scale (v0.1)"
Bibliographic record
Abstract
Morphological traits: predictable responses to macrohabitats across a 300 km scaleSpecies traits may provide a short-cut to predicting generalities in species turnover in response to environmental change, particularly for poorly known taxa.We ask if morphological traits of assemblages respond predictably to macrohabitats across a large scale.Ant assemblages were collected at nine paired pasture and remnant sites from within three areas along a 300km distance.We measured ten functional morphological traits for replicate individuals of each species.We used a fourth corner model to test associations between microhabitat variables, macrohabitats (pastures and remnants) and traits.In addition, we tested the phylogenetic independence of traits, to determine if responses were likely to be due to filtering by morphology or phylogeny.Nine of ten traits were predicted by macrohabitat and the majority of these traits were independent of phylogeny.Surprisingly, microhabitat variables were not associated with morphological traits.Traits which were associated with macrohabitats were involved in locomotion, feeding behaviour and sensory ability.Ants in remnants had more maxillary palp segments, longer scapes and wider eyes, while having shorter femurs, smaller apical mandibular teeth and shorter Weber's lengths.A clear relationship between traits and macrohabitats across a large scale suggests that species are filtered by coarse environmental differences.In contrast to the findings of previous studies, fine-scale filtering of morphological traits was not apparent.If such generalities in morphological trait responses to habitat hold across even larger scales, traits may prove critical in predicting the response of species assemblages to global change.
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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 | 0.009 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.523 | 0.339 |
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".