Prioritizing Odonata for conservation action in the northeastern USA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Odonata are valuable biological indicators of freshwater ecosystem integrity and climate change, and the northeastern USA (Virginia to Maine) is a hotspot of odonate diversity and a region of historical and growing threats to freshwater ecosystems. This duality highlights the urgency of developing a comprehensive conservation assessment of the region’s 228 resident odonate species. We offer a prioritization framework modified from NatureServe’s method for assessing conservation status ranks by assigning a single regional vulnerability metric (R-rank) reflecting each species’ degree of relative extinction risk in the northeastern USA. We calculated the R-rank based on 3 rarity factors (range extent, area of occupancy, and habitat specificity), 1 threat factor (vulnerability of occupied habitats), and 1 trend factor (relative change in range size). We combine this R-rank with the degree of endemicity (% of the species’ USA and Canadian range that falls within the region) as a proxy for regional responsibility, thereby deriving a list of species of combined vulnerability and regional management responsibility. Overall, 18% of the region’s odonate fauna is imperiled (R1 and R2), and peatlands, low-gradient streams and seeps, high-gradient headwaters, and larger rivers that harbor a disproportionate number of these species should be considered as priority habitat types for conservation. We anticipate that our analysis might serve as a model for guiding and standardizing conservation assessments at multiple scales for Odonata and other diverse taxa that have not yet received attention to prioritization.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it