The Pathway to Increase Standards and Competency of eDNA Surveys (PISCeS) 2023 conference—Towards standardization and data management in the field of eDNA
Bibliographic record
Abstract
The second iteration of the international conference "Pathway to Increase Standards and Competency of eDNA Surveys" was held at the University of Guelph, Guelph, Ontario, Canada from 18 June to 20 June 2023. During this environmental DNA (eDNA) conference, 60 oral and 25 poster presentations from academia, government, industry, NGOs, and Indigenous partners discussed the latest developments in eDNA research, explored strategies to inform public policy, and presented future directions in the field. The conference also included three panel discussions focused on prominent themes in the eDNA space, and five workshops dedicated to practical eDNA tools and methods. Recordings of presentations at the conference have been made available on YouTube. Here we summarize the major themes covered during the conference, provide our concluding remarks, and share the conference abstracts.
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 | 0.191 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".