LSL2002-23 Macrofauna Data. Version 1.0
Bibliographic record
Abstract
This dataset investigates the macrobenthos of the Canada Basin, which is one of the biologically least known areas of the Arctic deep sea. Macro-infauna was collected with 11 box cores at six stations ranging from 640 to 3250 m water depth between 16 August 2002 and 5 September 2002. Total abundance ranged from 3 to 265 individuals per 0.04 square meters (75 - 6625 individuals per square meter) and decreased with increasing depth. Biomass ranged from 0.04 to 228 mg wet weight per 0.04 m2 (1-5700 mg wet weight per square meter) and followed the same trend. Polychaetes, crustaceans and bivalves dominated the faunal densities, biomass and species numbers. Over 70 benthic invertebrate species from four biogeographic affinities were identified, including at least three new species of Isopoda. The evident low abundances and biomass are in agreement with findings from the Eurasian Arctic deep sea. This dataset is part of the Pacific Marine Arctic Regional Synthesis (PacMARS) Project. This data set is in Excel format, and presents the foundation for a scientific publication: Bluhm BA, MacDonald IR, Debenham C, Iken K (2005) "Macro- and megabenthic communities in the high Arctic Canada Basin: initial findings." Polar Biology (spec. issue) 28:218-231. Taxonomists that assisted with species identifications are listed in the publication. This data set has the unique catalog number 2005B1. This data set contains the raw counts for each box core replicate sample, mostly per 0.04 m2. In most cases, no replicates could be obtained due to time constraints. Limited environmental data as well as mean station totals of abundance and biomass are in the related publication. This dataset is not currently available online. For more information, email the point of contact.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.067 |
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".