Improving taxonomic resolution in large‐scale freshwater biodiversity monitoring: an example using wetlands and Odonata
Bibliographic record
Abstract
Abstract Immature aquatic insects are a major source of taxonomic difficulty in large‐scale freshwater biodiversity monitoring. Adult stages could improve taxonomic resolution for assessing distributions and trends of biodiversity. Odonata (dragonflies and damselflies) have accessible adult stages that should greatly enhance the amount of species‐level information. We used Odonata and a wetland monitoring programme in Alberta, Canada to illustrate how much taxonomic information can be lost in larval collections, and an extensive adult records database to estimate what could be gained from adult surveys. Despite processing 22 638 odonate specimens from 975 wetlands throughout Alberta, larval monitoring failed to collect or identify almost 60% of the lentic‐breeding Odonata species known from adult records. A total of 25 lentic‐breeding dragonfly species and 12 lentic‐breeding damselfly species were present in adult records and not the larval data, including species of conservation concern. Due to the abundance of early instars, a substantial 82% of the processed damselfly collection and 62% of the processed dragonfly collection was left at suborder. We recommend supplementing aquatic sampling with adult rearing, collecting, and observing (at least Odonata) to improve the basic inventory and overall status assessment in large‐scale freshwater biodiversity monitoring. This is especially true when aquatic sampling is restricted to a suboptimal time of year for species identifications.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".