DFO Maritimes Research Vessel Trawl Surveys Fish Observations
Bibliographic record
Abstract
This page has been Archived. The new versions of the datasets can be found at: Maritimes Summer Research Vessel Surveys http://ipt.iobis.org/obiscanada/resource?r=maritimes_summer_rv_surveys Maritimes Spring Research Vessel Surveys http://ipt.iobis.org/obiscanada/resource?r=maritimes_spring_rv_surveys Maritimes Fall Research Vessel Surveys http://ipt.iobis.org/obiscanada/resource?r=maritimes_fall_rv_surveys Maritimes 4VSW Research Vessel Surveys http://ipt.iobis.org/obiscanada/resource?r=maritimes_4vsw_rv_surveys This is the OBIS-formatted view of the Canadian Department of Fisheries and Oceans (DFO) Maritimes Research Vessel Trawl Surveys. This data is created using a Structured Query Language (SQL) program run against the DFO production database. Refer to the project section for more information about the surveys. This OBIS product reports the abundance and weight of demersal fish species captured by a variety of trawl gear and vessels. Catch data appear in the "individualcount" and "observedweight" OBIS fields (DwC dynamic properties field)and have been normalised for distance towed. The swept area of each tow can be found in the "samplesize" field of the OBIS schema (DwC dynamic properties field). Care should be taken when using multiple types of gear since the catchability of different fish species varies from gear to gear. The OBIS field "locality" contains the DFO stratum number where each tow was conducted. The OBIS field "collectioncode" identifies which survey series each data record comes from. Strata statistics required to derive stratified random estimates of catch abundance and biomass can be found on the OBISCanada website http://obiscanada.marinebiodiversity.ca/database/dfo-maritimes-survey-strata-statistics).
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.120 | 0.091 |
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".