Associated data to 'Variability in nitrogen-derived trophic levels of Arctic marine biota'
Bibliographic record
Abstract
Data on stable nitrogen isotopes for Arctic species, and corresponding trophic level parameters (See equation 1: δ15NBaseline, TLbaseline, Δ15N), were collected by conducting an extensive literature search using the Web of Science.〖TL〗_consumer=〖TL〗_baseline+(〖δ^15 N〗_consumer- 〖δ^15 N〗_baseline+ ∆D)/∆15N [1]We combined search strings related to stable isotope analysis (e.g. ‘stable isotope analysis’ and ‘nitrogen stable isotopes’) and biota in the European, Canadian and Alaskan Arctic using general terms (e.g. “Arctic biota”) as well as species names (e.g. ‘Ursus maritimus’ and ‘Calanus hyperboreus’). Stable isotope data were either extracted from tables or manually digitized using DigitizeIt (http://www.DigitizeIt.de/). Only stable isotope data sampled from April until October and after the year 2000 were included in the dataset, due to a lack of data outside this timeframe. Distinction was made between benthic and pelagic food webs. Although additional organism-specific and sample-specific parameters (i.e. age, length, body weight, date and sampling tissue) were included in the database, no further sub-setting was based on these parameters. The initial search resulted in 65 useful articles and reports, encompassing 148 species, covering four distinct Arctic areas: Alaskan Beaufort Sea, Canadian Beaufort Sea, Canadian Archipelago and Svalbard. Data pertaining to unique species (i.e. only observed in one of the four areas) were disregarded, resulting in a dataset comprising 107 species (29 pelagic, 78 benthic species), covering approximately 2400 individual records.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.060 | 0.054 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.013 |
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".