Species versus generic identifications in water-quality monitoring and ecological studies of Trichoptera in North America: Is this question still unresolved after half a century?
Bibliographic record
Abstract
Species-level identifications of the larval stages of caddisflies are available for only a limited number of taxa that are used currently in water-quality monitoring programs. This has been a long-lamented condition, but the proportion of species identifications available for aquatic forms that are used in these efforts has increased little over the past half-century. This is despite repeated and documented advantages that species-level identifications add to monitoring studies. Approaches to examining this question have ranged from anecdotal accounts of pollution tolerance among species within a genus to detailed analyses comparing information available through different hierarchical levels of taxonomy, from species to family. Justifications for not using species-level taxonomic resolution have ranged from financial considerations to suggestions that higher levels are equally as valid in showing trends as are species-level identifications. However, the evidence justifying the use of higher levels of taxonomy is lacking and more evidence favors the value of species-level identifications. Genetic techniques offer the promise of more larval-adult associations, more useful identification keys, and improved biological monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".