SBC LTER: OCEAN: Particulate Organic Matter Content and Composition of Stream, Estuarine, and Marine Sediments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An unprecedented five-year drought in California, coupled with conditions of anomalously low ocean productivity and the prospect of one of the strongest El Niño periods on record with above average rainfall were the impetus for this RAPID award, which seeks to test specific hypotheses pertaining to the origin, distribution, processing, and bioavailability of terrestrial organic matter in coastal marine sediments and their potential for serving as a reservoir of nitrogen storage to fuel nearshore primary production during periods when nitrate concentrations are low. The goals of the research were to: (1) measure bulk properties and biomarker tracers of particulate organic matter (POM) in stream water and in coastal marine sediments at SBC LTER and other reef sites differing in exposure to terrestrial runoff prior to and following large storm events, (2) determine the bioavailability of dissolved organic matter (DOM) released from POM in marine sediments following large runoff events, and (3) measure changes in concentrations of dissolved inorganic and organic nitrogen in pore water of marine sediments near to and distant from stream mouths in the Santa Barbara Channel. Samples were analyzed for organic matter content using a loss-on-ignition combustion method, and samples were also analyzed for organic carbon and nitrogen content and isotopes using a stable isotope mass spectrometer interfaced with an elemental analyzer. Subsamples were shipped to the laboratory of Marc Lucotte at the University of Québec, Montréal for analysis of lignin content using the cupric oxidation method.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 it