Peer Review #2 of "Integrating eDNA and citizen science observations to model distribution of a temperate freshwater turtle near its northern range limit (v0.2)"
Bibliographic record
Abstract
Background: To determine species distributions and the factors underlying them, reliable occurrence data are crucial.Assembling such data can be challenging for species with cryptic life histories or that occur at low densities. Methods:We developed species-specific eDNA protocols, from sampling through data interpretation, to detect the common musk turtle (Sternotherus odoratus) and tested whether eDNA occurrences change our understanding of the species distribution and the factors that shape its northern range limit.We used Species Distribution Models (SDMs) with full parameter optimization using citizen science observations of S. odoratus in Southern Ontario alone and together with eDNA occurrences.Results: Our eDNA protocol was robust and sensitive.SDMs built from traditional observations and those supplemented with eDNA detections were comparable in prediction accuracy.However, models with eDNA detections suggested that the distribution of S. odoratus in Southern Ontario is underestimated, especially near its northern range limit, and that it is shaped by thermal conditions, hydrology, and elevation.Our study underscores the promise of eDNA for surveying cryptic aquatic organisms in undocumented areas, and how such insights can help us to improve our understanding of species distributions.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.013 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.144 | 0.071 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".