Sea surface temperature data in False Creek, British Columbia
Bibliographic record
Abstract
This dataset is comprised of sea surface temperature data collected using HOBO TidbiT MX Temperature 400' data loggers (MX2203) deployed at nine locations in False Creek, British Columbia. Loggers were suspended at 0.5 metres depth from floating docks in proximity to settlement plates deployed for a Bioblitz in the fall of 2022, run by the Hakai Institute. The loggers were programmed to record the water temperature at 10 minute intervals, starting at 12:00 on July 12th, 2022 until removed from the water. Loggers were retrieved in early September, 2022 and recordings beyond each retrieval date are removed from the dataset. This data package includes: -Sea surface temperature data collected from loggers in False Creek between July 12th and mid-September, 2022. -Site metadata, including temperature logger location
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.019 | 0.023 |
| Research integrity | 0.003 | 0.014 |
| Insufficient payload (model declined to judge) | 0.234 | 0.015 |
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; both teacher heads 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".