DIDSON video collection of Coastal Lake Erie Wetland, Lucas Co, Ohio in 2011
Bibliographic record
Abstract
The data set includes quantitative fish abundance counts estimated from video data files collected by a Dual-frequency IDentification SONar (DIDSON) placed at the junction of a water control structure located between Pool 2B and Crane Creek (41.62133N, -83.20769W) within Ottawa National Wildlife Refuge (Lucas County, Ohio). One-hour-long video data files were used to estimate fish activity in a restored coastal wetland within Ottawa National Wildlife Refuge in the Western Basin of Lake Erie. Over 800 video hours of fish movement were recorded at the fully open fish passage structure; 136 video data files were chosen at random to represent the full video data file set and are available upon request. Fish counts from the DIDSON video data files using four different methods are provided, comparing two semi-automated and two manual counting methods. Fish tracking software Echoview paired with an "Automod" script from Milne Technologies (Keene, Ontario, Canada) as well as DIDSON viewer software were used to semi-automate the fish counting process and estimate the total number of fish passing through the structure within a video hour. Manual counts were performed by individuals counting fish from video playback given a specific set of criteria to follow for either the full hour of playback or a set of three two-minute intervals. In addition to the count data, calculated values of percent differences are included offering a comparison of the semi-automated and manual count values. Environmental data (turbidity) associated with the time periods captured in the video data files are also included.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".