“Swimming against the current”: Behavioral data of Betta splendens during an escape and avoidance task with water flows as the aversive stimulus
Bibliographic record
Abstract
(Hurtado-Parrado et al. 2019 https://doi.org/10.1016/j.beproc.2018.10.021). WFs with a duration of 10 s each were delivered with 30-s flow-flow (F-F) and response-flow (R-F) intervals in a custom-made shuttle tank. Fish escaped or avoided the WFs by changing compartments. Crossings during the WFs, interrupted the flows, were automatically scored as escape (Esc), and initiated a new R-F interval. Crossings that occurred during R-F or F-F intervals were scored as avoidance responses and also reset the R-F interval. We compared the effect of adding a warning stimulus - curtains of air bubbles - to the last 5 s of the R-F interval; i.e., signaled versus unsignaled avoidance. A unique development of the WFs procedure, and thus the data here described, is that crossings were further differentiated into subcategories; namely, early avoidance (EA) if a crossing occurred during the first 25 s of the R-F interval; late avoidance (LA) if a crossing occurred during the last 5 s of the R-F interval; and Flow-Flow avoidance (FF) if a crossing occurred anytime during the F-F interval. Here we present the data of six bettas across the different phases of the experiment; namely, baseline (BL - no WFs programmed), signaled avoidance (SA - warning stimulus scheduled), and unsignaled avoidance (UA - no warning stimulus scheduled). The dataset available at the Open Science Framework (OSF) repository (http://doi.org/10.17605/OSF.IO/FMHXD (Hurtado-Parrado et al., 2019)) includes for each fish and per 20-min daily session the total number of crossings; frequency of each type of crossing (Esc, EA, LA, FF); total WF frequency and duration, the total time spent in each compartment, and an index of preference for each compartment based on the proportion of time spent in the tank's compartments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".