Raw data of eDNA quantities from an experimental study testing temperature, water masses, and fish species
Bibliographic record
Abstract
Environmental DNA (eDNA) is a very promising approach to facilitate and improve the aquatic species monitoring, which is crucial for their management and conservation. In comparison with the plethora of monitoring studies in the fields, relatively few studies have focused on experimentally investigating the “ecology” of eDNA, in particular pertaining to processes influencing the detection of eDNA. The paucity of knowledge about its ecology hampers the use of eDNA analysis to its full potential. In this study, we experimentally evaluated the impact of several biotic and abiotic factors on the rate of production and degradation of eDNA. Individuals of three freshwater fish species (brown bullhead, tench, and yellow perch) with distinct ecology were placed in two types of water from the St. Lawrence River (Québec, Canada) with very distinct physicochemical characteristics and at three different temperatures. Water samples were then filtered at predetermined time intervals, and quantitative PCR was used to quantify the eDNA in each sample. We found that temperature, species, water types, and some interactions between these factors had a strong effect on the production and degradation of eDNA. The results of this study enhance our knowledge about the ecology of eDNA, thus improving eDNA data interpretation.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.016 |
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".