Intertidal barnacle recruitment (full paper published in 2016 in PeerJ 4: article e2623)
Bibliographic record
Abstract
On the Gulf of St. Lawrence coast of Nova Scotia (Canada), recruitment of the barnacle Semibalanus balanoides occurs in May and June. Every year in June between 2005 and 2016, we recorded recruit density for this barnacle at the same wave-exposed rocky intertidal location on this coast. During these 12 years, mean recruit density was lowest in 2015 (198 recruits dm-2) and highest in 2007 (969 recruits dm-2). The highest recruit density observed in a single quadrat was 1,457 recruits dm-2 (in 2011) and the lowest was 34 recruits dm-2 (in 2015). Most barnacle recruits appear during May, which suggests that most pelagic larvae (which develop over 5–6 weeks before benthic settlement) are in the water column in April. An AICc-based model selection approach identified sea surface temperature (SST) in April and the abundance of phytoplankton (food for barnacle larvae, measured as chlorophyll-a concentration –Chl-a–) in April as good explanatory variables. Together, April SST and April Chl-a explained 51% of the observed interannual variation in recruit density, with an overall positive influence. April SST was positively related to March–April air temperature (AT). April Chl-a wasnegatively related to the April ratio between the number of days with onshore winds (which blow from phytoplankton-limited offshore waters) and the number of days with alongshore winds (phytoplankton is more abundant on coastal waters). Therefore, this study suggests that climatic processes affecting April SST and April Chl-a indirectly influence intertidal barnacle recruitment by influencing larval performance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.112 | 0.038 |
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".