HLY-05-02 Macrofauna Data. Version 1.0
Bibliographic record
Abstract
This dataset contains soft bottom metazoan infauna (larger than 250 micrometer) that was collected with 33 box cores at 11 stations ranging from 817 m to 3961 m water depth in the high Arctic Canada Basin from 28 June to 25 July 2005. Total infauna abundance ranged from 0.6 to 50 individuals per 0.06 square meter (89-2722 individuals per square meter) and total biomass ranged from 0.2-1.6 g wet weight per 0.06 square meter (less than 0.1-26.1 g wet weight per square meter). Infauna taxon number per station was between 8 and 55. Abundance, biomass, taxon number and diversity indices of infauna declined significantly with water depth. Polychaetes, crustaceans and bivalves dominated the infaunal densities, biomass and species numbers. A total of 99 infauna taxa were identified, including at least three new species of polychaetes. This dataset is part of the Pacific Marine Arctic Regional Synthesis (PacMARS) Project. Multivariate analysis indicated that station groups "Chukchi slope," "Canada Basin abyss" and "Chukchi Cap" were moderately, but significantly, different in the infauna samples, but could not be similarly distinguished as such in the epifauna photographs. This data set contains the raw counts for each box core replicate sample, mostly per 0.06 m2 (20x30 cm) for replicates 1 and 2, and per 0.03 m2 for replicate 3. The second half of replicate three was used to sample for measurements of grain size, sediment organic carbon, etc. Those environmental data as well as mean station totals of abundance and biomass are in the related publication: MacDonald IR, Bluhm BA (shared 1st authorship), Iken K, Gagaev S, Strong S (2010) "Benthic macrofaunal and megafaunal assemblages in the Arctic deep-sea Canada Basin." Deep-Sea Research II 57:136-152. Species identifications were primarily conducted by the taxonomists listed in the data file.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.061 |
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".