eDNA Adoption: Weighing the Benefits and Challenges from Quebec Potential End-Users’ Perspective
Bibliographic record
Abstract
The collection of environmental DNA (eDNA) is a relatively new, non-invasive and effective method for detecting the presence of rare or endangered species, invasive alien species, and monitoring fish and wildlife populations, thus contributing to better conservation of natural environments. Academic researchers are its main users. The reasons for its slow diffusion among other potential users remain poorly documented to date. This study aimed to characterize the barriers and levers to the adoption of eDNA by distinct types of end-users, depending on the contexts in which they operate. We conducted semi-structured interviews with 33 participants to document and analyze their perceptions of eDNA. The Unified Theory of Acceptance and Use of Technology (UTAUT) inspired our analysis. Our findings revealed that potential end-users perceive the eDNA-based methods positively, although they are improvable. A lack of knowledge about its limitations and potential affects how useful eDNA is perceived to be and potential end-users' confidence in its results. We propose action levers to increase potential end-users' trust in the method and its compatibility with their current practices, and identify avenues to facilitate its diffusion.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".