Bibliographic record
Abstract
This is the OBIS extraction of the Pacific Aquatic Telemetry Hub (PATH) and California Department of Fish and Wildlife (CDFW) CDFWA15, consisting of the release tagging metadata, i.e. the location and date when the tagged animal was released, and summarized detection events of tagged individuals. If readers are interested in the source dataset they may also inquire with the project PIs as listed here or on the Ocean Tracking Network (OTN) portal for this project(https://members.oceantrack.org/project?ccode=PATH.CDFWA15). Abstract:Currently there is little known about rearing, migratory behavior, and general emigration patterns of juvenile southern Distinct Population Segment (sDPS) North American green sturgeon (Acipenser medirostris) and white sturgeon (Acipenser transmontanus) within the Lower Sacramento River, Sacramento-San Joaquin Delta (Delta), and San Francisco Bay. This data is crucial for improved species management in California’s Central Valley. To better understand emigration, California Department of Fish and Wildlife is undertaking a study to capture up to 100 green sturgeon and 100 white sturgeon annually and tag them with 69 kHz acoustic transmitters. Sampling is conducted by deploying and tending a 300-ft gill net with two-inch mesh. Acoustic transmitters are surgically implanted into the peritoneal cavity; tag size and therefore tag battery life is determined by fish weight and peritoneal cavity size. Passive integrated transponder (PIT) tags are also implanted in the peritoneal cavity during the surgical procedure. An array of 69 kHz acoustic receivers deployed throughout the lower Sacramento River and Sacramento-San Joaquin Delta collects detection data for juvenile sturgeon tagged with unique tag identification numbers. Telemetry data is analyzed to determine movement patterns, rearing habitat utilization, and migratory behavior within the Delta and outmigration to the Pacific Ocean as a function of water year type and juvenile age class.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.262 | 0.293 |
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".